Category: Healthcare

What Are Neural Implants?

Posted on by Jean Chalopin

Neural implants, also known as brain implants, have been the subject of extensive research in recent years, with the potential to revolutionise healthcare. These devices are designed to interact directly with the brain, allowing for the transmission of signals that can be used to control various functions of the body. 

While the technology is still in its early stages, there is growing interest in its potential applications, including treating neurological disorders, enhancing cognitive abilities, and even creating brain-machine interfaces. 

According to Pharmi Web, the brain implants market is expected to grow at a CAGR of 12.3% between 2022 and 2032, reaching a valuation of US$18 billion by 2032. 

During the forecast period, the market for brain implants is expected to experience significant growth, primarily due to the increasing prevalence of neurological disorders worldwide and the expanding elderly population. As the number of individuals in the ageing demographic continues to rise, so does the likelihood of developing conditions such as Parkinson’s disease, resulting in a surge in demand for brain implants.

This article will explore the technology behind neural implants and the benefits and considerations associated with their use.

Understanding Neural Implants

Neural implants are electronic devices surgically implanted into the brain to provide therapeutic or prosthetic functions. They are designed to interact with the brain’s neural activity by receiving input from the brain or sending output to it. These devices typically consist of a set of electrodes attached to specific brain regions, and a control unit, which processes the signals received from the electrodes.

The electrodes in neural implants can be used to either stimulate or record neural activity. Stimulating electrodes send electrical impulses to the brain, which can be used to treat conditions such as Parkinson’s disease or epilepsy. Recording electrodes are used to detect and record neural activity, which can be used for research purposes or to control prosthetic devices.

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To function correctly, neural implants require a control unit responsible for processing and interpreting the signals received from the electrodes. The control unit typically consists of a small computer implanted under the skin and a transmitter that sends signals wirelessly to an external device. The external device can adjust the implant’s settings, monitor its performance, or analyse the data collected by the electrodes.

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Neural implants can treat neurological disorders, including Parkinson’s disease, epilepsy, and chronic pain. They can also help individuals who have suffered a spinal cord injury or amputation to control prosthetic devices, such as robotic arms or legs.

The Benefits of Neural Implants

Neural implants have the potential to provide a wide range of benefits for individuals suffering from neurological disorders. These benefits include:

Improved quality of life. Neural implants can significantly improve the quality of life for individuals suffering from neurological disorders such as Parkinson’s disease, epilepsy, or chronic pain. By controlling or alleviating the symptoms of these conditions, individuals can experience greater independence, mobility, and overall well-being.

Enhanced cognitive abilities. Neural implants also have the potential to enhance cognitive abilities, such as memory and attention. By stimulating specific regions of the brain, neural implants can help to improve cognitive function, particularly in individuals suffering from conditions such as Alzheimer’s disease or traumatic brain injury.

Prosthetic control. Neural implants can also be used to control prosthetic devices, such as robotic arms or legs. By directly interfacing with the brain, these devices can be controlled with greater precision and accuracy, providing greater functionality and independence for individuals with amputations or spinal cord injuries.

Research. Neural implants can also be used for research purposes, providing insights into the workings of the brain and the underlying mechanisms of neurological disorders. By recording neural activity, researchers can gain a better understanding of how the brain functions and develop new treatments and therapies for a wide range of neurological conditions.

While there are significant benefits associated with neural implants, many challenges and considerations must be considered.

The Challenges

There are several challenges to consider regarding the use of neural implants.

Invasive nature. Neural implants require surgery to be implanted in the brain, which carries inherent risks such as infection, bleeding, and damage to brain tissue. Additionally, the presence of a foreign object in the brain can cause inflammation and scarring, which may affect the long-term efficacy of the implant.

Technical limitations. Neural implants require advanced technical expertise to develop and maintain. Many technical challenges still need to be overcome to make these devices practical and effective. For example, developing algorithms that can accurately interpret the signals produced by the brain is a highly complex task that requires significant computational resources.

Cost. Neural implants can be costly and are often not covered by insurance. This can limit access to this technology for individuals who cannot afford the cost of the implant and associated medical care.

Ethical considerations. Using neural implants raises several ethical considerations, particularly concerning informed consent, privacy, and the potential for unintended consequences. For example, there may be concerns about using neural implants for enhancement or otherwise incorrectly. 

Long-term durability. Neural implants must be able to function effectively for extended periods, which can be challenging given the harsh environment of the brain. The long-term durability of these devices is an area of active research and development, with ongoing efforts to develop materials and designs that can withstand the stresses of the brain. 

While the challenges associated with neural implants are significant, ongoing research and development in this field are helping to overcome many of these obstacles. As these devices become more reliable, accessible, and affordable, they have the potential to significantly improve the lives of individuals suffering from a wide range of neurological conditions.

Companies Operating in the Neural Implant Space

Several companies are developing neural implants for various applications, including medical treatment, research, and prosthetics. 

Neuralink, founded by Elon Musk, is focused on developing neural implants that can help to treat a range of neurological conditions, including Parkinson’s disease, epilepsy, and paralysis. The company’s initial focus is developing a ‘brain-machine interface’ that enables individuals to control computers and other devices using their thoughts.

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Blackrock Microsystems develops various implantable devices for neuroscience research and clinical applications. The company’s products include brain implants that can be used to record and stimulate neural activity and devices for deep brain stimulation and other therapeutic applications.

Medtronic is a medical device company that produces a wide range of products, including implantable devices for treating neurological conditions such as Parkinson’s, chronic pain, and epilepsy. The company’s deep brain stimulation devices are the most widely used for treating movement disorders and other neurological conditions.

Synchron is developing an implantable brain-computer interface device that can enable individuals with paralysis to control computers and other devices using their thoughts. The company’s technology is currently being tested in clinical trials to eventually make this technology available to individuals with spinal cord injuries and other forms of paralysis.

Kernel focuses on developing neural implants for various applications, including medical treatment, research, and cognitive enhancement. The company’s initial focus is developing a ‘neuroprosthesis’ that can help treat conditions such as depression and anxiety by directly stimulating the brain.

Closing Thoughts

The next decade for neural implants will likely see significant technological advancements. One central area of development is improving the precision and accuracy of implant placement, which can enhance the efficacy and reduce the risks of these devices. Another area of focus is on developing wireless and non-invasive implant technologies that can communicate with the brain without requiring surgery.

Machine learning and artificial intelligence advancements are also expected to impact neural implants significantly. These technologies can enable the development of more sophisticated and intelligent implants that can adapt to the user’s needs and provide more effective treatment. Additionally, integrating neural implants with other technologies, such as virtual and augmented reality, could lead to exciting new possibilities for treating and enhancing human cognitive function.

The next decade for neural implants will likely see significant progress in the technology and its applications in treating a wide range of neurological and cognitive conditions. However, ethical and regulatory considerations must also be carefully considered as the field advances.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Precision Medicine and AI With Blockchain

Posted on by Jean Chalopin

Precision medicine has emerged as a promising approach to providing personalised treatments for patients based on their genetic makeup, lifestyle, and environment. However, this approach requires vast amounts of data to be collected, analysed, and securely shared among healthcare providers and researchers. 

Artificial intelligence (AI) and blockchain technology offer potential solutions to these challenges by enabling data-driven and secure decision-making. According to a recent report by Market.us, the global precision medicine market is projected to reach $254 billion by 2032, growing at a compound annual growth rate (CAGR) of 21.1% from 2023 to 2032. 

This article will explore how AI and blockchain are transforming precision medicine and improving patient outcomes.

What Is Precision Medicine?

Precision medicine, or personalised medicine, is a healthcare approach that tailors medical treatments to individual patients based on their genetic information, environmental factors, lifestyle, and other personal characteristics. Unlike the traditional ‘one-size-fits-all’ approach, precision medicine aims to provide targeted and effective treatments that can improve patient outcomes and reduce healthcare costs.

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To achieve this, precision medicine requires vast amounts of data to be collected, analysed, and shared securely among healthcare providers and researchers. This is where AI and blockchain technology comes in. AI can analyse large datasets and identify patterns and correlations that human analysts may miss. At the same time, blockchain technology can provide a secure and transparent platform for sharing and accessing data.

AI can also help drug discovery by analysing large genomic, proteomic, and metabolomic data datasets to identify new drug targets and develop personalised treatments. For example, AI algorithms can analyse patients’ genomic data and predict their likelihood of responding to a particular drug or developing adverse effects.

What Is Blockchain?

Blockchain is a distributed ledger technology that enables secure, transparent, and tamper-proof record-keeping of transactions and data. It uses cryptographic techniques to create an unalterable chain of blocks that contains a record of all transactions and data entered into the system. The chain is maintained by a network of nodes, each of which has a copy of the ledger, and any changes to the ledger must be validated and approved by the network.

Blockchain technology supports precision medicine in several ways.

Firstly, blockchain provides a secure and tamper-proof platform for storing and sharing patient data. In a traditional healthcare system, patient data is stored in a centralised database vulnerable to data breaches and hacking attacks. In contrast, blockchain technology uses a decentralised system, making it difficult for hackers to breach the system and steal sensitive patient information.

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Furthermore, blockchain technology ensures the privacy and confidentiality of patient data by using cryptographic techniques to encrypt patient data. Patient data is stored in blocks linked together using cryptographic hashes, creating an unalterable and transparent ledger of patient data. Authorised parties can only access this ledger with the necessary permissions, and any changes made to the ledger are recorded and visible to all authorised parties.

Blockchain technology can also support clinical trials and drug discovery by providing a secure and transparent platform for sharing data among researchers and healthcare providers. Clinical trials often involve collecting large amounts of sensitive patient data, which researchers must share securely to ensure patient privacy and confidentiality. Blockchain technology can provide a secure and transparent platform for sharing data among researchers while ensuring the privacy and confidentiality of patient data.

Another advantage of using blockchain technology in precision medicine is the ability to create smart contracts. Smart contracts are self-executing contracts that use blockchain technology to automate the negotiation and execution of contractual terms. In precision medicine, smart contracts can be used to create agreements between patients, healthcare providers, and researchers that specify how patient data will be collected, analysed, and shared. The blockchain can automatically enforce these agreements, ensuring that all parties adhere to the agreed-upon terms.

Why Does Precision Medicine Need AI and Blockchain?

AI and blockchain technology each play a crucial role in enabling processes that enhance the effectiveness of precision medicine.

AI enables the analysis of large and complex datasets in a timely and efficient manner, identifying intricate patterns and correlations. With AI, healthcare providers and researchers can develop more accurate and personalised treatments based on a patient’s unique characteristics. However, without secure and transparent platforms for sharing data, the effectiveness of AI in precision medicine would be limited.

Understanding the Precision Medicine Sector

Several companies are leading the field in precision medicine, each with its own unique approach to this innovative field. 

One example is 23andMe, a personal genomics and biotechnology company offering consumers genetic testing and analysis services. 23andMe provides insights into an individual’s ancestry, genetic health risks, and carrier status for certain inherited conditions. The company aims to empower individuals with knowledge about their genetic makeup and help them make informed decisions about their health.

Another example of a company leading the field in precision medicine is Foundation Medicine, a molecular information company specialising in the genomic profiling of cancer patients. The company’s genomic tests help oncologists match patients with targeted therapies and clinical trials based on the genetic characteristics of their tumours. The goal is to provide more personalised and effective cancer treatments.

IBM Watson Health is a health information technology company that uses machine learning and artificial intelligence to help healthcare providers make better clinical decisions. The company’s offerings include genomics, imaging, clinical trial matching tools, and population health and patient engagement solutions.

GRAIL is a biotechnology company that is developing a blood test for the early detection of cancer. The test analyses fragments of DNA that are shed by tumours into the bloodstream, to detect cancer at an earlier stage when it is more treatable. The test is currently being evaluated in large-scale clinical trials.

Finally, Veracyte is a genomic diagnostics company that focuses on providing molecular diagnostic tests for thyroid and lung cancer. The company’s tests help healthcare providers make more informed treatment decisions, reducing unnecessary surgeries and treatments. These companies are just a few examples of the many innovative organisations leading the way in precision medicine, using cutting-edge technologies and approaches to improve patient outcomes and transform healthcare.

Considerations With Precision Medicine

When it comes to precision medicine, some technical, regulatory, clinical and ethical considerations need to be taken into account.

Technical

  • Advanced data analysis techniques like machine learning and natural language processing are needed to extract valuable insights from large and complex datasets.
  • There is an additional need for secure and interoperable data-sharing platforms to enable collaboration among healthcare providers and researchers.

Ethical

  • It’s vital to ensure the privacy and confidentiality of patient data and obtaining informed consent from patients for using their data.
  • The potential for data analysis and interpretation bias could result in inaccurate or discriminatory treatment decisions.
  • Providers must ensure equitable access to precision medicine technologies, while addressing disparities in healthcare access and outcomes. 
  • Hurdles exist within the ownership of patient data, as well as the potential for private companies’ commercialization of patient data.

Regulatory

  • Paramount remains the need for compliance with relevant laws and regulations, such as those related to data protection, patient rights, and clinical trials.
  • The industry requires regulatory oversight and approval of precision medicine technologies and treatments.

Clinical

  • There are concerns surrounding the validation and verification of precision medicine treatments, as their safety must first be verified. 
  • However, we must integrate precision medicine into clinical workflows and decision-making processes, while providing specialised training. 

Social

  • Precision medicine impacts society as a whole, including its potential to exacerbate existing health disparities or lead to the creation of new ones.
  • The potential for precision medicine to contribute to the democratisation of healthcare and the empowerment of patients, as well as its role in shaping public policy and healthcare delivery models.

Despite the many considerations that need to be taken into account, precision medicine is still considered a revolutionary field in healthcare. The ability to tailor medical treatments and interventions to individual patients based on their unique genetic, environmental, and lifestyle factors can transform healthcare in previously unimaginable ways.

Closing Thoughts

Precision medicine promises more personalised and effective treatments, earlier disease detection, and improved patient outcomes. While technical, ethical, regulatory, clinical, and social considerations must be addressed, precision medicine’s potential benefits cannot be ignored. 

As researchers and healthcare providers continue to work on developing and implementing precision medicine technologies and treatments, it is essential to carefully consider the implications of these innovations and ensure that they are used responsibly.

As healthcare becomes more personalised and patient-centred, the ability to tailor medical treatments and interventions to individual patients will become increasingly important. Moreover, precision medicine can reduce healthcare costs by avoiding unnecessary treatments and improving the efficiency of clinical trials and drug development. 

As our understanding of the genetic, environmental, and lifestyle factors that contribute to disease continues to improve, precision medicine will become an increasingly important tool in the fight against complex and chronic diseases.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Why Life Expectancy Is Increasing

Posted on by Jean Chalopin

Our average life expectancy has increased from 45 years in the 1850s to nearly 80 years today as a result of medical science. Researchers believe that our life spans will continue to grow, but there is an eventual hard limit.

Advances in medicine that are driving this lengthening life span range across a vast spectrum, including diagnostic developments, medical devices, prescription drugs, and procedures.  These medical interventions are joined with healthier lifestyles, a more holistic approach to medicine, and more accurate and earlier diagnoses.

We will take a look at how medical science and technological advances have contributed to our lengthening lifespans.  

Healthy Lifestyles and Life Expectancy

We are increasingly more conscious of the need to maintain a healthy lifestyle. Such a lifestyle comes in the form of improved diet and nutrition, exercising regularly, maintaining our mental and emotional health, and regularly assessing our health.

The healthy lifestyle trends that started with the 1970s running craze and subsequent 80s aerobics craze have more recently grown into fitness and healthcare wearables that allow consumers to monitor their personal health–keeping track of steps, activity, sleep, heart rate, stress, and other vital signs. 

The IDC predicts that the total wearable market will grow at a rate of 13.4% in the next five years, with an expected 219.4 million units being sold in 2022.  

The wearables of today span multiple medical and health functions, from fitness trackers to smart health watches, including wearable ECG devices, and blood pressure monitors, biosensors, and more. These devices can collect physical and medical data with various levels of usefulness. They can monitor, analyse, and even predict health and mental well-being when paired with mobile and desktop applications.  

The covid pandemic accelerated a growing trend toward telehealth and remote monitoring.  This trend can be leveraged to move us in the direction of preventative healthcare for conditions such as heart disease and stroke.

There are now several wearable makers in the healthcare space, including Interplex, that has been a supplier to many manufacturers and disruptive wearable companies. They have a diabetes monitoring system that can help keep patients’ blood sugar levels more standard.

Courtesy of Interplex

Wearables and Health Diagnoses

Wearables are no longer new to the market, and their usefulness and quality have consistently improved. They collect multiple data points related to one’s health, and when applying professional analysis to the collected data, they are now able to make early detection possible, which helps with disease prevention and in proposing better treatments. Currently, medical laboratories are providing up to 70% of lab testing to physicians in order to provide accurate diagnoses and treatment plans.

Clinical lab testing results for diagnostic decision-making are an essential part of clinical medicine. The selection of laboratory tests available to doctors has grown exponentially since they first surfaced in the 1920s. Now a wide array of tests can diagnose, screen for, and research disease, while others can monitor treatments and therapies to ensure effectiveness.  It is now possible to design tests and equipment that fit the exact specifications needed for medical diagnosis, and this has moved into the area of genetic diseases.

Medical Treatment

Advances seen in medical equipment and treatment protocols have contributed to improvements in patient outcomes. A particular area of advancement is in surgical treatment.  This advancement is mainly in a movement toward more precise surgical operations as well as minimally invasive procedures.  

Equipment now being used can make cuts with lasers with high precision enabling delicate surgeries to be performed on the brain or eye, or they can even focus radio or other waveforms that ultimately produce surgical-like outcomes below the skin, without the surgeon having to make a single incision. 

With these advances, minimally invasive but advanced laparoscopic surgery (keyhole), hysteroscopic surgery, and myomectomies are just a few of the procedures that have resulted from advancements in medical technology. Other medical fields have benefitted from these advances, including neurology, interventional equipment, and cardiology.  

Beyond surgical precision and minimal invasiveness, mobile medical technologies are advancing, bringing medical technology and equipment into more hospitals, doctors’ offices, emergency rooms, and even homes, making a significant contribution to medical treatment and health outcomes.  

Telemedicine

Healthcare professionals are increasingly using mobile medical equipment and devices from medical workstations to specialised equipment for telehealth, to deliver medical care to their patients wherever they may be (both patients and medical professionals).

Through the increase in transportable and telehealth solutions, mobile medicine is expanding the reach of healthcare far beyond the traditional hospital and clinical setting.  The fields of teleradiology, telenephrology, and telepsychiatry are just a few examples of mobile medicine that have now become more commonplace and will likely continue to grow over the next decade.  With advancing technology, more of these “tele” medical fields will be available and contribute to a significant change in the medical industry. 

Courtesy of the CCHP

In the future, doctors with specialties will be able to practise much of their medicine from anywhere in the world, not needing to see their patients in person directly. This will be aided by virtual reality, augmented reality, and machines capable of testing, diagnosis, and even surgery from afar. The possibilities are endless in this space, and with 5G and soon-to-be 6G, much of this advancement we will likely see over the next two decades.  

IoT Devices

Advancements in low-cost sensor tech, dependability, increased data storage and transmission capabilities, and low power consumption has meant that new devices will be possible that can change how we view medicine. With the increasing number of IoT devices coming to market that are connecting our homes, businesses, supply chains, and vehicles, we will also see similar devices for ourselves.  

These devices will initially monitor specific health issues, allowing us to identify when a specific problem is occurring and potentially deal with it automatically. This is already happening with Implantable Cardioverter Defibrillators (ICDs).  

ICDs are being implanted into patients. In the case of a cardiac event, they are informing medical professionals of a problem and providing a shock to the patient that will restart their hearts and save their lives.  

Courtesy of the American Heart Association

These kinds of devices will expand with the IoT and become more common for many of our common ailments.  

In the future, we will likely see devices with multiple functions, such as monitoring, aiding, and preventing devices all in one, able to identify many different ailments when they first become a problem and treat them before they grow in severity.

Life Expectancy

Much of the life expectancy gains we have seen over the past 150 years have been due to improvements in infant mortality and the advent of antibiotics and immunizations. Now that 1 in 5000 Americans is 100 or over, researchers are investigating the ageing process and how to slow it.

According to biologist Andrew Steele, the author of Ageless, we have been treating medicine in an unsystematic way. We have been focused on the endpoints of ageing, problems like heart disease and cancer, but not addressing the fundamental causes for these maladies.  

But this is changing, and medicine is slowly shifting to a holistic approach where we first understand these hallmarks but then come up with treatments that intervene with them directly. This would mean a switch to preventative treatments, which can proceed earlier in life and stop people from getting age-related diseases in the first place.  

For example, treatments for cellular senescence (chronic inflammation) already exist that target many redundant cells by killing them, preventing them, or removing them from the body, along with a toxic set of molecules that accompany them, contributing to cancer and heart disease. 

These drugs have been shown to help extend the lives of mice, with fewer cancers, cataracts, and heart disease, even making them less frail as they age. Eventually, these same drugs may be given to humans.

Closing Thoughts

We have made several advances in medical science that have extended our lifespans and made us healthier. The technology we are now creating is directly impacting our health, being more connected with our doctors, and allowing us and them to receive information sooner–keeping us healthier.

In the coming decades, we will likely use genetic engineering to prevent genetic diseases from appearing at all. It is an exciting time for the medical field, and we, as patients, will most certainly be the beneficiaries.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Can AI and Biotech Conquer Death?

Posted on by Jean Chalopin

Death is a certainty that all living beings must face, but what if we could beat it? The advances in biotechnology and artificial intelligence, or AI, have raised the question of whether death can be conquered. 

This article explores the potential of biotech and AI to achieve immortality, farfetched as it seems, and the pros and cons associated with this idea.

Advancements in Biotech

Biotechnology is a rapidly evolving field that is focused on using biological processes, systems, and organisms to create new products, technologies, and solutions. Significant advancements in biotech have led to the development of treatments and therapies that can extend human life. One of the most promising areas of biotech research is the development of stem cell therapies.

Stem cells are undifferentiated cells that can differentiate into different types of cells and tissues in the body. Stem cell therapies involve transplanting stem cells into damaged or diseased tissues to regenerate and repair them. This can be used to treat various conditions, including spinal cord injuries, heart disease, and Parkinson’s disease.

Another area of biotech research that has the potential to extend human life is gene therapy. Gene therapy involves introducing genetic material into a patient’s cells to treat or prevent disease.

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This can be used to treat genetic disorders, such as cystic fibrosis, and to prevent age-related diseases, such as Alzheimer’s disease.

Advancements in AI

AI focuses on developing machines that can perform tasks that typically require human intelligence, such as perception, learning, reasoning, and decision-making. AI can transform many industries, including healthcare, by providing new tools and solutions to improve patient outcomes and extend human life.

One of the most promising applications of AI in healthcare is precision medicine. Precision medicine involves genetic and other data to tailor medical treatments to individual patients. AI can be used to analyse vast amounts of data to identify patterns and insights that can be used to develop personalised treatment plans.

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Another area of research that can potentially extend human life is the development of autonomous medical systems. Autonomous medical systems are machines that can perform medical tasks without human intervention. These systems can be used to monitor patient health, administer medications, and perform surgical procedures.

AI and Biotech

AI can help progress the biotech industry in fields such as stem cell treatment and gene therapy, which we mentioned above. 

While stem cell treatment has shown promise in treating a range of diseases, it is still a relatively new field, and much is not yet understood about how stem cells work and how they can be effectively used in therapy.

AI can play a vital role in advancing stem cell treatment by helping to identify the best type of stem cell for a given condition and optimising the conditions under which the stem cells are grown and differentiated into specific cell types. It can also help to predict the likelihood of success for a given stem cell therapy and identify potential side effects or complications.

One company that is working on using AI to advance stem cell treatment is Insilico Medicine. The company uses AI to develop new drugs and therapies for various diseases, including cancer, fibrosis, and ageing. The company’s platform uses deep learning algorithms to analyse large amounts of data and identify potential drug targets and therapies. The video below is an explainer of one of their products. 

Similarly, gene therapy is a new field that can benefit from AI; which can play a crucial role in advancing gene therapy by helping to identify the best targets for gene therapy and optimising the delivery of genes to the target cells. It can also help predict gene therapy’s potential outcomes and identify possible side effects or complications.

One company that is working on using AI to advance gene therapy is Homology Medicines. The company is developing gene therapies for various genetic diseases, including phenylketonuria (PKU) and sickle cell disease. The company’s platform uses AI to design and optimise the delivery of gene therapies to make gene therapy more effective and accessible.

The Issues With Conquering Death

The idea of conquering death with biotech and AI has several potential benefits and risks. Some of the most significant pros and cons are outlined below.

Pros

  • Improved quality of life: Conquering death could significantly improve the quality of life for older adults. We could eliminate many of the problems associated with ageing, such as chronic diseases and disability. 
  • Advancements in science and technology: Immortality could lead to significant advances in science, technology, and culture by allowing our brightest minds to continue contributing to society.
  • Increased productivity: If people lived indefinitely, they would have more time to contribute to society, leading to increased productivity and economic growth.

Cons

  • Overpopulation: One of the most significant risks associated with conquering death is the potential for overpopulation. With people living indefinitely, the world’s population would continue to grow, straining resources and exacerbating environmental issues.
  • Unequal distribution of access: Ethical considerations are associated with unequal access to immortality technology. If only the wealthy and powerful could access these technologies, it could exacerbate existing inequalities.
  • Loss of cultural traditions: Immortality could lead to the loss of cultural traditions and the stagnation of cultural evolution.

The Challenges With Conquering Death

While AI and biotech hold significant promise for advancing medicine and extending human life, many challenges must be overcome before death can indeed be conquered.

One of the primary challenges is the ethical implications of using these technologies to extend life. While many people would welcome the opportunity to live longer, healthier lives, there are concerns about the potential consequences of such an advancement. 

For example, there may be questions about who would have access to these technologies and how they would be distributed. There may also be concerns about the impact on the planet and the potential strain on resources if the population continues to grow as people live longer.

Another challenge is the complexity of the human body and the many factors that can impact health and longevity. While AI and biotech can help identify potential therapies and treatments, much is still not yet understood about how the body works and how it can be effectively treated. 

For example, there are many different types of cancer, each with unique characteristics and challenges. Developing effective therapies for each type of cancer will require a deep understanding of the underlying biology and a willingness to experiment with new approaches.

There are also challenges related to developing and regulating new therapies and treatments. Developing new drugs and therapies is a long and expensive process. There is always a risk that a promising treatment will fail in clinical trials or have unforeseen side effects. In addition, there are regulatory challenges related to getting new therapies approved and ensuring they are safe and effective for humans.

Finally, there are challenges related to using AI and biotech in healthcare. For example, there may be concerns about the accuracy and reliability of AI algorithms, particularly when making decisions about human health. There may also be questions about how AI and biotech will impact the roles of healthcare providers and whether machines in the future will replace them.

Market Statistics and Use Cases

The biotech and AI industries are rapidly growing and have significant potential to transform healthcare and extend human life. According to a report by Grand View Research, the global biotech market size was valued at $1,023.92 billion in 2021 and is expected to grow at a compound annual growth rate (CAGR) of 13.9% from 2022 to 2030. The report cites the growing demand for biopharmaceuticals and increasing investment in biotech research as key market growth drivers.

Several companies are working in biotech and AI to develop new therapies and solutions that can extend human life. One such company is Unity Biotechnology, which focuses on developing therapies targeting the underlying causes of age-related diseases. The company’s lead program is a senolytic therapy that targets senescent cells, which are cells that have stopped dividing and contribute to age-related diseases.

Another company in the biotech space is Moderna, best known for developing one of the first COVID-19 vaccines. The company is also working on developing mRNA therapies that could be used to treat a range of diseases, including cancer and rare genetic disorders.

In AI, several companies are developing solutions to improve patient outcomes and extend human life. One such company is Deep Genomics, which uses AI to create new therapies for genetic diseases. The company’s platform combines genomics and machine learning to identify genetic mutations that cause disease and develop new therapies to treat them.

Closing Thoughts

The idea of conquering death with biotech and AI is a tantalising prospect but comes with significant challenges and risks. While biotech and AI have the potential to extend human life and improve the quality of life in old age, there are also substantial ethical considerations associated with immortality. 

As these two industries continue to evolve, it is crucial to consider the challenges and risks and work towards developing solutions that can extend human life sustainably and ethically. The goal is to improve the quality of life for all, not just for some. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees. 

Longevity and the Future

Posted on by Jean Chalopin

With continuous advancements in medical technology, the science of longevity has seen incredible progress in the past few decades. According to the World Health Organization, the global average life expectancy increased from 64.2 years in 1990 to 72.6 years in 2019. 

The same report states that, in high-income countries, life expectancy at birth can reach up to 80 years. With ongoing research and advancements, there is a high probability that the average life expectancy will continue to rise in the future. In this article, we will explore the advances in the science of longevity, including the latest discoveries, potential future developments, and ethical considerations.

The Science of Longevity

The primary goal of longevity research is to improve the quality of life by extending the number of healthy years an individual can enjoy. 

Several research areas contribute to the science of longevity, including genetics, epigenetics, stem cell research, and nutrition. Recent studies show that our lifestyle habits and environment also significantly determine our life span. 

Lifestyle Habits

Studies show that our lifestyle habits and environment can significantly impact our lifespan. For example, a study published in the American Journal of Clinical Nutrition found that eating a diet rich in fruits, vegetables, whole grains, nuts, and legumes reduces mortality risk from all causes, including cardiovascular disease and cancer.

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Similarly, a study published in the British Medical Journal found that quitting smoking can add up to 10 years to a person’s life expectancy. The study also found that even those who quit smoking in their 60s can still add several years to their lifespan.

Other studies have looked at the impact of exercise on lifespan. A study published in the journal PLOS Medicine found that individuals who engaged in regular physical activity had a reduced risk of premature death from all causes, including cardiovascular disease and cancer.

Stress is also a factor that can impact lifespan. A study published in the journal ‘Science’ found that chronic stress can accelerate ageing at the cellular level by shortening telomeres. The study suggests that stress management techniques like mindfulness meditation and yoga may help slow ageing and extend lifespan.

These studies demonstrate that our lifestyle habits and environment can significantly impact our lifespan. Making healthy lifestyle choices, such as eating a nutritious diet, quitting smoking, engaging in regular physical activity, and managing stress, can help to extend our healthy years and improve our overall quality of life.

Genetic Research

Genetic research has made significant progress in identifying the genes contributing to ageing and age-related diseases. Studies have identified several genetic variants associated with an increased risk of Alzheimer’s, cancer, and heart disease. 

Researchers are also exploring the potential of gene editing technologies, such as CRISPR, to modify genes associated with ageing and disease.

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One study published in Nature Genetics found a genetic variant associated with an increased risk of Alzheimer’s disease that affects the immune system’s ability to clear beta-amyloid protein from the brain. 

Beta-amyloid protein is a hallmark of Alzheimer’s disease. Another study published in the journal Nature Communications identified a genetic variant associated with an increased risk of heart disease that affects the metabolism of fats in the liver.

Epigenetics Research

Epigenetics is the study of changes in gene expression without altering the underlying DNA sequence. Recent research has shown that epigenetic changes can significantly impact ageing and age-related diseases. 

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For example, a study published in Aging Cell found that specific epigenetic changes in the brain are associated with cognitive decline in ageing adults. Another study published in Nature Communications found that DNA methylation changes in the blood are associated with ageing and age-related diseases, such as cancer and cardiovascular disease.

Stem Cell Research

Stem cell research focuses on developing therapies to regenerate damaged tissues and organs. Recent advancements in stem cell research have shown promising results in animal studies, including restoring damaged heart tissue and reversing age-related muscle loss.

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A study published in the journal Cell Stem Cell found that injecting old mice with muscle stem cells from young mice improved muscle function and strength in the older mice. Another study published in the journal Nature found that transplanting neural stem cells into the brains of ageing mice improved cognitive function.

Nutrition Research

Nutrition research has shown that a healthy diet can significantly impact our lifespan. Studies have shown that diets high in fruits, vegetables, whole grains, and lean protein can reduce the risk of chronic diseases and improve overall health. Researchers are also exploring the potential of calorie restriction and intermittent fasting to extend lifespan.

Case Study in Okinawa

The Okinawan population in Japan is a fascinating case study in the science of longevity. Okinawa is known for having one of the highest percentages of centenarians in the world, with a significant number of individuals living beyond 100. Researchers have been studying the factors that contribute to the long lifespan of Okinawans for many years.

One of the critical factors that researchers have identified is the Okinawan diet, which is high in fruits, vegetables, and whole grains and low in calories and saturated fat. The traditional Okinawan diet consists of sweet potatoes, vegetables, tofu, seaweed, and fish. The diet is rich in antioxidants and anti-inflammatory compounds, which may help to reduce the risk of chronic diseases such as cardiovascular disease and cancer.

Regular physical activity is another factor that contributes to the longevity of Okinawans. Many Okinawans engage in physical activity, such as walking, gardening, and traditional martial arts practices. This physical activity may help to reduce the risk of age-related diseases and maintain physical function in old age.

Social connections are also a crucial factor in the longevity of Okinawans. Many Okinawans maintain strong social connections throughout their lives, which can provide emotional support and a sense of purpose. Studies have shown that social isolation is associated with increased mortality risk and poor health outcomes, emphasising the importance of social connections for overall health and longevity.

In addition to these lifestyle factors, genetic and environmental factors may also contribute to the longevity of Okinawans. Researchers have identified several genetic variations that may play a role in the long lifespan of Okinawans, including variations in genes related to insulin sensitivity and inflammation. Environmental factors, such as low pollution levels and high exposure to natural light, may also contribute to the longevity of Okinawans.

Potential Future Developments

The future of longevity research looks promising, with ongoing advancements in medical technology and genetic analysis. Here are some potential future developments in the field of longevity. 

Anti-Aging Drugs

Several drugs that can delay ageing and age-related diseases are currently in development. These drugs work by targeting specific genes and proteins that are associated with ageing and age-related diseases.

Gene Editing

Gene editing technologies such as CRISPR can potentially modify genes associated with ageing and disease. Researchers are exploring the potential of these technologies to extend lifespan and reduce the risk of age-related diseases.

Regenerative Therapies

Regenerative therapies such as stem cell treatments have shown promising results in animal studies. Researchers are exploring the potential of these therapies to regenerate damaged tissues and organs in humans.

Artificial Intelligence

Artificial intelligence (AI) can potentially revolutionise the field of longevity research. AI can analyse large datasets and identify patterns to help researchers develop new therapies and treatments.

Ethical Considerations

The potential to extend lifespan raises several ethical considerations that must be addressed. One concern is the unequal distribution of life-extending therapies. 

If these therapies are only available to the wealthy, it could widen the gap between the rich and the poor. Another concern is the potential for overpopulation and strain on resources if the population continues to age and live longer. Researchers and policymakers must consider these ethical implications as they develop new therapies and treatments.

Closing Thoughts

In conclusion, the science of longevity has made significant progress in recent years, thanks to advancements in medical technology and research. Genetic, epigenetics, stem cell, and nutrition research have contributed to our understanding of ageing and age-related diseases. 

Future developments in anti-ageing drugs, gene editing, regenerative therapies, and artificial intelligence promise to extend a healthy lifespan. However, researchers must also consider the ethical implications of extending lifespan, including unequal distribution of therapies and strain on resources. With ongoing research and advancements, the future looks bright for the science of longevity.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Ageing and AI

Posted on by Jean Chalopin

The field of longevity medicine is reinvigorating. Until recently, the initiative of slowing and reversing ageing was not considered ‘proper science’ or a reasonable use of public funds. 

Ageing was regarded as an unavoidable and permanent component of the human condition. It was not a disease but rather a susceptibility to disease that develops over time and cannot be reversed. The medical establishment viewed people who argued against it as entertaining mavericks at best and disreputable charlatans at worst.

The sneering has not gone away entirely, but there is now a significant investment in addressing ageing, rather than just the diseases that emerge with age, such as cancer, heart attacks, and Alzheimer’s.

Funding and approval are in place for the TAME (Targeting Aging With Metformin) project, showing that the medical profession is engaging in tackling the ageing process. 

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Billionaire cryptocurrency community members are emerging as the most prominent anti-ageing promoters, such as Richard Heart. The interest from influential figures could lead to sizable donations in the field and lead the fight against ageing. 

Artificial Intelligence Addresses Longevity

Applying modern artificial intelligence techniques to healthcare, particularly deep neural nets and reinforcement learning, is one reason for the shift in attitudes toward ageing. Neural networks are data-processing algorithms that work in layers, with each layer taking data from the previous layer as input and passing an output up to the next layer.

The outcomes do not have to be binary, but they can be weighted. Reinforcement learning algorithms adapt their approach in response to feedback from their surroundings.

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A significant contribution of AI towards anti-ageing is something known as ageing clocks. 

Age is much more than how many birthdays you’ve had. Stress, sleep, and diet all have an impact on how our organs deal with the wear and tear of daily life, which may cause you to age faster or slower than people born on the same day. That means your biological age may differ significantly from your chronological age—the number of years you’ve lived.

Your biological age is more likely to reflect your physical health and even mortality than your chronological age. However, calculating it is far from simple. Scientists have spent the last decade developing ageing clocks, which analyse markers in your body to determine your biological age.

The basic idea behind ageing clocks is that they will tell you how much your organs have degraded and, thus, how many healthy years you have left. The accuracy of hundreds of ageing clocks developed in the last decade, on the other hand, varies greatly. And scientists are still debating an important question: What does it mean to be biologically young?

When an AI is trained to predict age using specific types of biological data, it learns biology. The hope is that these AIs will eventually help us understand how ageing works.

How Does AI in Ageing Work?

AI will analyse the vast amounts of health data we collect, including time series (longitudinal) data and comparative data across social and national groups. The patterns it uncovers in the data will lead to the generation of hypotheses to test. The ageing clocks will reveal treatments’ effectiveness – or lack thereof.

AI can identify trends and determine casualties by analysing patterns in medical data, images, and other sources.

AI algorithms analysed daily photographs of mice in a project to extract potential ageing markers and develop lifespan control solutions. The same learning process for visual biomarkers can then be applied to other species, including humans.

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In addition to image analysis algorithms, AI can detect recurring patterns in human ageing to identify biomarkers. AI can identify ageing trends in different populations by sifting through mass data on various blood tests, retinal scans, muscle analyses, and more, or by comparing human data to other species.

Longevity and Psychological Age

Some academics believe that psychological age defines us much more than biological age. 

People age at different rates, and their ageing dynamics are shaped and defined by their mindset and environment. Our psychological state, in turn, influences our perception of time and health status. 

An individual’s psychological age, also known as subjective age, is based on how young or old they perceive themselves to be compared to their chronological age, depending on an individual’s self-assessment of the degree of ageing and how this perception influences their overall well-being.

Exciting research on AI-powered engines reveals novel tools that can estimate one’s psychological age and future well-being based on a psychological survey. Essentially, these AI tools can be used to determine ways to improve and maximise long-term well-being based on information inferred from available datasets.

There are two models that exist as predictors of chronological and subjective age, considering around 50 psychological features. Both clocks had modifiable features that could be altered through social and behavioural interventions. More importantly, it may be useful in shifting personal perceptions of ageing toward a mindset that promotes productive and healthy behaviours.

AI has made it possible to find the best path to emotional stability. As previously stated, this new deep learning model can predict a person’s current psychological age and guide future well-being by providing personalised recommendations for improving mental resilience. 

The model’s SOM offers a set of non-trivial, personalised paths to improved well-being that can be used as a reference for the cognitive behavioural therapy and online mental health approaches. Perhaps this tool can be used as a supplement or stand-alone approach to adjusting one’s psychological age and providing emotional and mental stability.

Closing Thoughts

AI can recommend health solutions and identify potential issues by analysing the effects of therapeutic treatments, preventive measures, different lifestyles, and other factors.

On a more fundamental level, understanding how proteins and cells respond positively or negatively to treatments allows AI to contribute to the efficient development of medicine.

Deep learning’s power may soon help us alleviate the discomforts of biological ageing.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

What Is Messenger RNA?

Posted on by Jean Chalopin

The flow of genetic information is an essential process in biology that involves the transfer of genetic material from DNA to RNA to proteins. At the heart of this process is a molecule called messenger RNA (mRNA). 

Without mRNA, cells would not be able to create the proteins necessary for various cellular processes, and genetic information would not be able to flow from the nucleus to the cytoplasm.

This article discusses what mRNA is and its role in molecular biology. 

Defining Messenger RNA

Messenger RNA is a ribonucleic acid (RNA) molecule that plays a central role in the flow of genetic information in cells. mRNA molecules are transcribed from DNA in the cell nucleus and carry the genetic information encoded in the DNA to the ribosomes, the cellular organelles responsible for protein synthesis.

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The mRNA molecule is a single-stranded RNA molecule complementary to the DNA sequence from which it was transcribed. The sequence of the mRNA molecule is determined by the order of nucleotides in the DNA template strand, with each three nucleotides, called a codon, corresponding to a specific amino acid. The sequence of amino acids, in turn, determines the sequence of the protein that will be synthesised.

mRNA synthesis is initiated by binding an enzyme called RNA polymerase to a specific region of the DNA called the promoter. The RNA polymerase then unwinds the double helix structure of the DNA and begins transcribing the DNA sequence into a complementary mRNA molecule. As the mRNA molecule is synthesised, it is processed to remove non-coding regions called introns and join the coding regions, called exons, to create a mature mRNA molecule.

The mature mRNA molecule is then exported from the nucleus and travels to the cytoplasm, where it binds to ribosomes. The ribosome reads the sequence of nucleotides on the mRNA molecule in groups of three, each corresponding to a specific amino acid. As the ribosome moves along the mRNA molecule, it synthesises a protein by joining its amino acids in the order specified by the mRNA sequence.

mRNA in Therapeutic Intervention

Messenger RNA has become an essential tool in therapeutic intervention due to its ability to control protein expression and serve as a template for the production of specific proteins. mRNA-based therapies offer several advantages over traditional protein-based therapies and small molecule drugs.

First, mRNA-based therapies are highly specific, encoding the exact protein of interest. This specificity allows for targeted therapies that selectively block a disease-causing protein’s activity or replace a missing or defective protein.

Second, mRNA molecules are rapidly degraded in cells, allowing for precise control over the protein expression level. This feature makes mRNA-based therapies more flexible and adaptable than traditional protein-based therapies, where the protein dosage can be challenging to regulate.

Finally, mRNA molecules can be modified chemically to improve their stability, increase their efficiency, and target them to specific cells or tissues. These modifications allow for greater control over the delivery and distribution of the mRNA molecule and its encoded protein.

mRNA Vaccines

One of the most significant advances in messenger RNA-based therapies has been the development of mRNA vaccines, such as the Pfizer-BioNTech and Moderna COVID-19 vaccines. These vaccines use mRNA molecules encoding the spike protein of the SARS-CoV-2 virus to stimulate an immune response against the virus. 

The mRNA is encapsulated in lipid nanoparticles and delivered to cells, where it is translated into the spike protein. The immune system recognizes the protein as foreign and produces antibodies against it, protecting against infection.

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Messenger RNA-based therapies have also shown promise in treating various diseases, including cancer, where they can be used to induce the expression of tumour-suppressing proteins or target cancer cells for destruction by the immune system. In one recent study, mRNA was used to encode chimeric antigen receptor (CAR) T cells, genetically engineered immune cells that target and destroy cancer cells. The mRNA-encoded CAR T cells were highly effective in killing cancer cells in vitro and in mouse leukaemia models.

mRNA-based therapies offer a powerful new disease treatment and prevention approach, potentially revolutionising medicine.

mRNA Use Cases

There are several use cases for mRNA-based therapies.

Cancer Immunotherapy

In a study published in Nature, researchers used mRNA to program immune cells to attack cancer cells. The mRNA encoded a chimeric antigen receptor (CAR) that recognized and targeted cancer cells. When the mRNA was delivered to immune cells in vitro, the resulting CAR T cells were highly effective in killing cancer cells. The researchers suggest that this approach could be used to develop personalised cancer immunotherapies.

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Genetic Disease Therapy

In a study published in the New England Journal of Medicine, researchers used mRNA to treat a patient with cystic fibrosis. The mRNA encoded a functional copy of the CFTR gene, which is mutated in patients with cystic fibrosis. The mRNA was delivered to the patient’s lungs via nebulisation. The treatment improved lung function and reduced respiratory symptoms, suggesting that mRNA-based therapies could effectively treat genetic diseases.

Vaccine Development

The development of mRNA-based vaccines has been one of the most exciting recent applications of mRNA technology. The Pfizer-BioNTech and Moderna COVID-19 vaccines, which use mRNA to encode the spike protein of the SARS-CoV-2 virus, have been highly effective in preventing COVID-19 infection. 

A study published in the New England Journal of Medicine found that the Pfizer-BioNTech vaccine was 95% effective in preventing COVID-19 infection in clinical trial participants.

These case studies demonstrate the potential of messenger RNA-based therapies in various applications, including vaccines, cancer immunotherapy, and gene therapy.

Risks With mRNA

Despite the promise of messenger RNA (mRNA) as a powerful tool for therapeutic intervention, there are several risks and challenges associated with its use.

One of the primary concerns is the potential for off-target effects, where the mRNA molecule produces unintended proteins or triggers an immune response against normal cells. This risk can be minimised through careful selection and design of the mRNA molecule and its delivery system, but it remains a significant challenge in developing mRNA-based therapies.

Delivering mRNA molecules to their target cells presents another challenge. mRNA is a large, hydrophilic molecule that rapidly degrades in the bloodstream and other extracellular fluids. Delivering mRNA molecules to cells is essential for their effectiveness in protein translation. Researchers have developed several approaches to overcome this challenge, such as utilising lipid nanoparticles to protect mRNA molecules from degradation.

Another risk associated with mRNA-based therapies is the potential for immune system activation or adverse reactions. Though these are generally mild and short-lived, mRNA vaccines have been associated with side effects such as injection site pain, fever, and fatigue. More serious adverse events have been reported in rare cases, such as anaphylaxis.

Finally, there is a risk that the rapid degradation of mRNA molecules in cells could limit the effectiveness of mRNA-based therapies. mRNA molecules have a relatively short half-life in cells, which could limit the duration of protein expression and the therapeutic effect. However, this risk can be mitigated by using modified mRNA molecules that are more stable or by administering multiple doses of the mRNA over time.

Closing Thoughts

While mRNA-based therapies offer many exciting disease treatment and prevention possibilities, they also present significant challenges and risks. Careful consideration and planning will be required to maximise the potential benefits of mRNA-based therapies while minimising the risks.

However, recent case studies have demonstrated the potential of mRNA-based therapies in various applications, including vaccines, cancer immunotherapy, and gene therapy. The development of mRNA-based therapies represents an exciting frontier in biomedicine, with the potential to revolutionise how we treat and prevent disease.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Drug Discovery and AI

Posted on by Jean Chalopin

There is very little that is efficient in drug discovery and development. Approximately 90% of medications that reach commercialisation fail. Each one costs more than $1 billion and takes ten years to develop.

However, technical developments in data collection are advancing artificial intelligence in drug discovery, which might open the door to discovering treatments for disorders that have eluded medical researchers for millennia.

How Does Drug Discovery Work? 

Drug discovery procedures are often drawn-out and laborious. Academic or industrial scientists create molecules. They search for ‘targets’ (such as proteins), where the molecule can go in the body to deliver treatment.

Researchers must ensure that the molecule doesn’t mistake a healthy protein for a target. Otherwise, a drug floating about in the body may bind to and destroy a healthy cell, causing a poisonous effect. Once a target is obtained, it is removed from the body and tested against molecules in the laboratory to see what sticks.

However, when clinical trials move forward, many of these medications fail due to unanticipated toxicity in the body or the drug itself not performing as well in people as it did in the lab. This is why most investments fail. 

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Fortunately, artificial intelligence (AI) can improve the efficiency of drug discovery. 

AI Improves Drug Discovery

Platforms for drug discovery can more accurately forecast the effects of drugs early on by utilising data. AI links molecules with targets and models how they will behave within the body, increasing the likelihood that they will survive clinical trials and reduce patient toxicity rates.

Although the impact of AI on conventional drug discovery is still in its infancy, when AI-enabled capabilities are added to a traditional process, they can significantly speed up or otherwise improve individual steps and lower the costs of conducting expensive experiments. AI algorithms can alter the majority of discovery jobs (such as the design and testing of molecules) so that physical trials are only necessary to confirm findings.

Pharma companies are partnering with AI drug discovery platforms, with Amgen and Generate Biomedicines announcing a deal worth up to £1.9 billion in 2022.  

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Pharma businesses need to prepare for a future in which AI is frequently utilised in drug research, given the revolutionary potential of AI. The applications are many, and pharma businesses must decide where and how AI can best contribute. 

New players are ramping up quickly and providing considerable value. In practice, this entails taking the time necessary to comprehend the full impact that AI is having on R&D. This includes separating hype from real accomplishment and realising the distinction between standalone software solutions and end-to-end AI-enabled drug discovery.

The AI-First Approach

There are five elements to an AI-first approach in drug discovery.

Vision and Strategy

Companies must create an AI roadmap that outlines specific, high-value use cases compatible with certain discovery initiatives. Focus and prioritisation are crucial. 

Businesses should choose a limited number of use cases that are dispersed throughout the various research stages. Otherwise, AI will be viewed as a sideline and not directly related to the company’s R&D strategy or financial objectives.

Technology and Data

Prior to creating a complete tool or platform, concentrate on developing a proof-of-concept algorithm: the bare minimum analysis that verifies your capacity to draw insightful conclusions from your data in a particular scientific environment. If the insights prove worthwhile, you can spend money industrialising the tool and improve the user interface.

AI Partnerships

To be the preferred partner for big AI players, pharma companies must adopt new behaviours and ways of working. Partnerships are a powerful strategy for accelerating AI discovery and building a true value proposition. 

Companies should consider how they share data, what their culture looks like in the context of AI, and how quickly they can adapt their business model to new technology. Although those things may not seem critical to the business, they will be for potential AI partners. 

Internal Resource Management

Data scientists and engineers are a unique breed. They do not always fit into companies and cultures that are primarily focused on medicine.

But pharmaceutical businesses need more than just expertise in data science and software. Senior decision-makers will probably need to be trained on how AI-generated recommendations are made if only to stop suggestions from being revalidated using conventional methods. To interpret and adequately test the results of the algorithms, medical scientists must be knowledgeable about the analytical methodologies required but not necessarily fluent in them.

Drug Discovery Datasets

Data is pivotal for successful AI and machine learning deployments. Large-scale datasets help to build models for machine learning that can evaluate whether molecules have investable potential. 

Researchers can quantify the strength of molecules in binding to a protein. Specific drug interactions and combinations do not react favourably and must be avoided by patients. Vast data volumes help to identify the positive and negative combinations quickly. 

Embedding AI Within Drug Discovery

AI represents a new era in drug discovery. Companies will need to couple a clear vision with a healthy amount of ambition to be successful. 

Choose an area to apply AI and be specific about the improvements you want to see. Decide whether to alter the discovery program using an AI-first model or to utilise AI for optimising the present discovery process. 

Further, it can be challenging to scale AI. Teams frequently adhere to well-established procedures and feel at ease using instruments with a long history of success. Businesses need to demonstrate their commitment to AI by focusing on complete processes and thoroughly reevaluating current operating methods. 

It’s best to include the entire organisation in the AI journey. In order to overcome hesitancy, management should emphasise the transformational vision, share value proofs and lessons from inside teams, and gradually develop a wave of enthusiasm.

AI is here for good, and it is for us to harness its power. What can be better than using AI to cure previously unsolvable diseases?

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

AI in Healthcare

Posted on by Jean Chalopin

When you think about technological breakthroughs from history, the full promise is never what it initially does but what it eventually enables. If you go as far back as the steam engine, it cost far more than other power sources when first commercialised. However, as soon as it enabled faster transportation and cheaper product shipping, suddenly, it did not seem so expensive. 

AI in healthcare is the modern-day steam engine. Although applications are still relatively sparse, the fourth industrial revolution of data and digital is starting to enable the new future. 

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The market for artificial intelligence in healthcare, estimated to be worth USD 10.4 billion in 2021, is anticipated to increase at a CAGR of 38.4% from 2022 to 2030. Key factors propelling the market’s expansion are the expanding datasets of digital patient health information, the desire for individualized treatment, and the rising demand for lowering healthcare costs.

The Current State of AI in Healthcare

Despite having the highest healthcare spending in the world, the United States now has inferior individual health outcomes than most other industrialised countries.

People of all generations need healthcare that is tailored to their requirements. Millennials want to be able to order their meals and receive medical advice from the same place—their sofa. In contrast, groups like the baby boomer generation take a totally different tack. 

They are far more likely to want a primary care physician, so we can move away from these systems’ one-size-fits-all approach to actual care delivery–toward leveraging data and AI to genuine care.

For AI to be successful in the 21st century, there are three vital components.

Responsibility

Sometimes, problems are unsuitable for AI; deciphering intent is paramount. Similarly, poor data and algorithm management might unintentionally introduce biases into analyses, with negative consequences for people.

Competence

Innovations must function, and the health ecosystem must agree on what constitutes an acceptable margin of error. The same forgiveness that is extended to a human physician who makes a single error is not extended to computer systems that prescribe cancer therapies.

Transparency

Being open about the limits of data and AI in healthcare can aid in the maintenance of confidence in the face of imperfect performance.

Early adopters of AI in healthcare have already enabled breakthroughs paving the way for a shift from scepticism to a beginning of trust, as well as a jump from efficiency to better efficacy.

Use Cases for AI in Healthcare

There are several ways in which AI is influencing the healthcare sector. 

Medical Diagnoses

Misdiagnosis is a significant problem in the healthcare industry. According to recent research, around 12 million people in the United States are misdiagnosed yearly, with cancer patients accounting for 44% of them. AI is assisting in overcoming this problem by increasing diagnostic accuracy and efficiency.

AI-enabled digital medical solutions, such as computer vision, provide accurate analysis of medical imaging, such as patient reports, CT scans, MRI reports, X-rays, mammograms, and so on, to extract data that is not apparent to human eyes.

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While AI can analyse most medical data quicker and more accurately than radiologists, it is still not sophisticated enough to replace radiologists.

Automation in Patient Care

Poor communication is seen as the worst aspect of the patient experience by 83% of patients. AI can assist in overcoming this obstacle.

AI can automate reminders, payment issues and appointment management. Clinicians can spend more time caring for patients than doing administrative work. AI can also do a lot of the background work of analysing data and ensuring patients are assigned to the correct doctor or department. 

AI in Surgery

Healthcare robot AI is making procedures safer and smarter. In complex surgical operations, robotic-assisted surgery allows doctors to attain more precision, safety, flexibility, and control.

It also allows for remote surgery to be conducted from anywhere in the world in locations where surgeons are not available. This is especially true during worldwide pandemics when social distance is required.

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The primary benefits of robotic surgery include the following:

  • Reduction in hospital stay time after a procedure
  • Reduced pain relative to human-operated surgery
  • Decreased chance of post-surgery complications

Sharing Medical Data

Another advantage of using AI in healthcare is its capacity to handle enormous volumes of patient data.

Diabetes, for example, affects more than 10% of the US population. Patients may watch their glucose levels in real-time and get data to manage their progress with doctors and support personnel using tools like the FreeStyle Libre glucose monitoring device driven by AI.

Research and Development

AI has a wide range of applications in medical research. It can help to find new drugs or repurpose existing ones. In this example, AI was used to analyse cell images and understand which were most effective for patients with specific diseases. A conventional computer is slow to spot the differences that AI can find in seconds. 

Staff Training

AI tutors can provide instant feedback to students, allowing them to learn skills safely and effectively. In the example, students could learn skills 2.6 times faster and 36% better than those who are not taught with AI.  

Virtual patients can help with remote training. During the pandemic, AI supported skill development remotely when group gatherings were impossible. 

AI-based apps are being created to aid nurses in various ways, including decision support, sensors to alert them of patient requirements, and robotic assistance in difficult or dangerous circumstances.

Overcoming Challenges with Healthcare AI

There are some best practices to follow for healthcare sector incumbents to overcome the barriers associated with AI and seize the opportunities. 

First, systems must be explainable. You don’t want to be in a position where an AI system detects cancer, and the radiologist cannot explain the decision. Prioritise building hybrid explainable AI.

AI-powered medical diagnoses are accurate but not flawless. AI systems can make mistakes that have profound implications. More testing of your AI models is a smart strategy to improve accuracy and reduce false positives. 

Due to privacy and ethical limitations in the healthcare industry, gathering training medical data might be complex. Even when automated, this procedure can be costly and time-consuming. Investing in privacy-enhancing technology can help reassure users that their data is safe when acquiring and processing sensitive medical data.

Another critical obstacle to adopting AI in healthcare is patient resistance. At first sight, robotic surgery may frighten patients, but their reservations may dissipate when they learn about the benefits. To solve this dilemma, patients must be appropriately educated.

Closing Thoughts

Clinicians need to become aware of the potential of this new technology and grasp that the world is changing. It is readily adapting AI to improve the patient experience, to eliminate errors, and to ultimately save more lives. 

In a human-centric field such as medicine, AI can never fully replace doctors–their care, empathy, touch, and years of experience. What AI can do, today, is eliminate the barriers to delivering care in a globalising, rapidly growing world that is falling behind with its healthcare. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

The Impact of AI on Insurance in 2023

Posted on by Jean Chalopin

The impact of artificial intelligence (AI) in the insurance industry has been significant in the past five years. According to Accenture, the adoption of AI in the insurance sector is expected to double in the next three years. It could lead to cost savings of up to $1.2 billion annually. 

Another study by PwC found that 63% of insurance companies have already implemented or are planning to implement AI in their business operations. As a result, AI has allowed insurance companies to improve customer experience, reduce costs, and streamline underwriting processes, leading to a more efficient and profitable industry.

What Is AI in Insurance?

Artificial intelligence (AI) in insurance refers to using advanced technology, such as machine learning, natural language processing, and computer vision, to automate and optimise various functions in the insurance industry. 

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This includes underwriting, claims processing, fraud detection, and customer service. By leveraging AI, insurance companies can analyse vast amounts of data, make predictions, and provide personalised services to customers in real-time. The integration of AI has the potential to transform the insurance industry by improving efficiency, reducing costs, and enhancing the customer experience.

Why Does Insurance Need AI?

There are several reasons why the insurance industry needs AI in 2023 and beyond:

  1. Increased efficiency and cost savings: AI can automate manual processes and help insurers analyse large amounts of data quickly and accurately, leading to faster decision-making and cost savings.
  2. Improved customer experience: AI can provide personalised recommendations and real-time support to customers, helping insurance companies meet their evolving needs and preferences.
  3. Enhanced risk assessment: AI can analyse historical data and other relevant information to identify and assess risk factors, helping insurers make more informed decisions.
  4. Fraud detection: AI can help insurers detect fraudulent activities by analysing patterns and anomalies in data, reducing the risk of financial losses.
  5. Better decision-making: AI can provide actionable insights to insurance companies, helping them make more informed decisions and improve their operations.

Given AI’s numerous benefits, it is likely that the insurance industry will continue to adopt and integrate this technology in the coming years. This article will examine some of these areas in more detail.

The Challenge for Legacy Insurers

Legacy insurers must invest in AI to keep up with fintech start-ups because these start-ups often have a technology-first approach and can offer innovative, personalised services to customers in real time. AI can help legacy insurers automate manual processes, provide better customer experiences, and make more informed decisions, allowing them to compete with fintech start-ups and remain relevant in the market.

An example of a legacy insurer investing in AI is AXA, one of the world’s leading insurance companies. AXA has integrated AI into its operations, using machine learning to automate manual processes, improve risk assessment, and provide personalised recommendations to customers. 

Another example is Allianz, which has invested in AI to enhance its underwriting processes and improve its efficiency. These companies recognise the importance of AI in staying competitive and relevant in the market and are taking steps to integrate this technology into their operations.

The Rise of Gen Z

Insurance companies need to invest in AI to keep up with the demands of Gen Z tech-savvy buyers who demand fast, convenient, and personalised experiences. AI can help insurance companies automate manual processes, provide real-time support, and deliver personalised recommendations, meeting the demands of this demographic.

Insurance companies use AI to analyse social media data and understand customer preferences and behaviours to meet Gen Z’s demands. For example, an insurance company might use AI to analyse customer interactions on social media platforms, such as Facebook or Instagram, to determine which products and services are most relevant to them.

One insurance company that is already using social media to its advantage for Gen Z is Lemonade. The company has built a chatbot that uses natural language processing to handle customer inquiries and uses AI algorithms to process claims quickly.

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Using AI to understand customer behaviour and preferences, Lemonade can provide a personalised experience that appeals to Gen Z buyers. This demonstrates how insurance companies can use AI to keep up with the demands of this demographic and remain competitive in a rapidly evolving market.

AI for Efficiency and Cost Savings

Insurance companies use AI to improve efficiency and reduce costs by automating manual processes and making more informed decisions. AI algorithms can quickly analyse large amounts of data, identify patterns, and make predictions, allowing insurance companies to make more informed decisions and reduce the time and resources required to complete tasks.

For example, some insurance companies use AI to automate the underwriting process, reducing the time and resources required to assess risk and provide quotes. Others use AI to automate claims processing, reducing the time required to process claims and improving the overall customer experience.

MetLife is one insurance company already utilising AI to increase efficiency and save costs. AI has been incorporated into the company’s operations, with algorithms used to automate procedures, enhance risk assessment, and deliver tailored suggestions to consumers. Using AI, MetLife can improve customer service, save operating expenses, and increase operational efficiency. 

Improving Customer Service

Insurance companies use AI to improve the customer experience by providing more personalised services and real-time support. For example, AI algorithms can analyse customer data and preferences to provide tailored recommendations, and chatbots powered by natural language processing can provide instant customer support. These technologies allow insurance companies to provide a faster, more convenient, and more personalised experience, meeting the demands of modern customers.

One insurance company already using AI to improve customer experience is Oscar Health. The company uses AI to personalise the customer experience, from identifying and addressing potential health issues to providing care recommendations. Oscar Health uses machine learning algorithms to analyse customer data, such as claims and health records, to identify potential health issues and provide personalised recommendations to customers.

By using AI to provide a more personalised experience, Oscar Health can meet its customers’ demands and provide a level of service that sets it apart from other insurance providers. This demonstrates how insurance companies can use AI to improve the customer experience and remain competitive in a rapidly evolving market.

Enhancing Risk Assessments

Insurance companies use AI to enhance risk assessment by providing more accurate and reliable data analysis. AI algorithms can quickly analyse large amounts of data, identify patterns, and make predictions, allowing insurance companies to make more informed decisions and better assess risk. This helps insurance companies reduce fraud risk and underwrite policies more effectively, improving the overall customer experience.

One insurance company already using AI to enhance risk assessment is Allstate. The company uses AI algorithms to analyse customer data, such as driving patterns and vehicle usage, to assess risk and provide personalised insurance coverage. Allstate’s AI system can quickly process large amounts of data and identify patterns that might indicate increased risk, allowing the company to make more informed decisions and better assess risk. 

Using AI to enhance risk assessment, Allstate can provide better customer service and remain competitive in a rapidly evolving market. This demonstrates how insurance companies can use AI to drive operational efficiency, reduce costs, and improve customer experience.

Using AI to Detect Fraud

Insurance companies use AI to detect fraud by analysing large amounts of data to identify suspicious patterns and anomalies. AI algorithms can quickly process data, identify red flags, and trigger investigations, helping insurance companies prevent fraud more effectively.

One insurance company that is using AI to detect fraud is Anthem. The company uses AI algorithms to analyse customer data, such as claims and payment history, to identify suspicious patterns and trigger investigations. Anthem’s AI system can quickly process large amounts of data and identify red flags, such as unusual billing patterns or repeated claims from the same provider, allowing the company to detect fraud more effectively. 

Using AI to detect fraud, Anthem can reduce the risk of financial losses and improve the overall customer experience. This demonstrates how insurance companies can use AI to enhance security, reduce costs, and remain competitive.

Creating Actionable Insights

Insurance companies use AI to create actionable insights by analysing large amounts of data to identify patterns, make predictions, and inform business decisions. AI algorithms can quickly process data, identify trends, and provide real-time insights, allowing insurance companies to make data-driven decisions and improve the overall customer experience. 

Some examples of insurance companies using AI to create actionable insights include Allstate, Metromile, and Lemonade. 

Allstate uses AI to assess risk and provide personalised insurance coverage by analysing customer data, such as driving patterns and vehicle usage. Metromile uses AI to analyse telematics data from connected vehicles to provide real-time insights and inform pricing and underwriting decisions. Finally, lemonade uses AI to automate the insurance process, making it faster and more efficient to create actionable insights that drive business decisions and improve the overall customer experience.

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Closing Thoughts

AI is having a significant impact on the insurance industry, transforming the way that insurance companies operate and interact with customers. AI is helping insurance companies to improve efficiency, reduce costs, enhance risk assessment, detect fraud, and create actionable insights. 

In the next ten years, the use of AI will likely continue to grow, leading to more advanced and sophisticated applications of AI in areas such as underwriting, claims to process, and customer service. 

As AI becomes more prevalent in the insurance industry, we will likely see a shift towards more data-driven, personalised, and automated insurance services that deliver improved customer outcomes and increased efficiency for insurers. With the continued growth of AI in insurance, the industry will continue to evolve and adapt to meet the changing needs of customers and remain competitive in a rapidly changing market.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

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