Healthcare - Trusting Disruption by Deltec

Can AI and Biotech Conquer Death?

Posted on May 15, 2023May 15, 2023 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.

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.

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 May 5, 2023May 5, 2023 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.

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.

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.

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.

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.

Ageing and AI

Posted on April 28, 2023April 28, 2023 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.

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.

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.

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.

What Is Messenger RNA?

Posted on April 18, 2023April 18, 2023 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.

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.

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.

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.

Drug Discovery and AI

Posted on April 13, 2023April 13, 2023 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.

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.

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?

AI in Healthcare

Posted on April 5, 2023April 5, 2023 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.

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 21^st 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.

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.

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.

The Impact of AI on Insurance in 2023

Posted on February 28, 2023February 28, 2023 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.

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:

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.
Improved customer experience: AI can provide personalised recommendations and real-time support to customers, helping insurance companies meet their evolving needs and preferences.
Enhanced risk assessment: AI can analyse historical data and other relevant information to identify and assess risk factors, helping insurers make more informed decisions.
Fraud detection: AI can help insurers detect fraudulent activities by analysing patterns and anomalies in data, reducing the risk of financial losses.
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.

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.

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.

AI’s Transformation of Oncology

Posted on January 17, 2023January 17, 2023 by Jean Chalopin

Artificial intelligence (AI) is constantly reshaping our lives. It saves companies and us time and money, but it has applications that can be applied to medicine, potentially saving our lives.

We can understand AI’s evolution and achievements to model future developmental strategies. One of AI’s most significant medical impacts is already being seen in and will continue in oncology.

AI has opened essential opportunities for cancer patient management and is being applied to aid in the fight against cancer on several fronts. We will look into these and see where AI can best aid doctors and patients in the future.

Where Did AI Come From?

Alen Turing first conceived the idea of computers mimicking critical thinking and intelligent behavior in 1950, and by 1956 John McCarthy came up with the term Artificial Intelligence (AI).

AI started as a simple set of “if A then B” computing rules but has advanced dramatically in the years since, comprising complex multi-faceted algorithms modeled after and performing similar functions to the human brain.

AI and Oncology

AI has now taken hold in so many aspects of our lives that we often do not even realize it. Yet, it remains an emerging and evolving model that benefits different scientific fields, including a pathway of aid to those who manage cancer patients.

AI has a specific task that it excels at. It is especially good at recognizing patterns and interactions after being given sufficient training samples. It takes the training data to develop a representative model and uses that model to process and aid decision-making in a specific field.

When applied to precision oncology, AI can reshape the existing processes. It can integrate a large amount of data obtained by multi-omics analysis. This integration is possible because of advances in high-performance computing and several novel deep-learning strategies.

Notably, applications of AI are constantly expanding in cancer screening and detection, diagnosis, and classification. AI is also aiding in the characterization of cancer genomics and the analysis of the tumor microenvironment, as well as the assessment of biomarkers for prognostic and predictive purposes. AI has also been applied to follow-up care strategies and drug discovery.

Machine Learning and Deep Learning

To better understand the current and future roles of AI, two essential terms fall under the AI umbrella that must be clearly defined: machine learning and deep learning.

Machine Learning

Machine learning is a general concept that indicates the ability of a machine (a computer) to learn and therefore improve patterns and models of analysis.

Deep Learning

On the other hand, deep learning is a machine learning method that utilizes algorithmic systems that mimic a system of biological neurons called deep networks. When finalized, these deep networks have high predictive performance.

Both machine and deep learning are central to the AI management of cancer patients.

Current Applications of AI in Oncology

To understand the roles and potential of AI in managing cancer patients and show where the future uses of AI can lead, here are some of the current applications of AI in oncology.

With the below charts, “a” refers to oncology and related fields and “b” to types of cancers for diagnosis. +

*Courtesy of the* *British Journal of Cancer*; a. oncology and related fields: cancer radiology 54.9%, pathology 19.7%, radiation oncology 8.5%, gastroenterology 8.5%, clinical oncology 7.0%, and gynecology 1.4% b. tumor types: general cancers 33.8%, breast cancer 31.0%, lung cancer 8.5%, prostate cancer 8.5%, colorectal cancer 7.0%, and brain tumors 2.8%, others: 6 tumor types, 1.4% each.

The above graph, from the British Journal of Cancer, summarizes all FDA-approved artificial intelligence-based devices for oncology and related specialties. The research found that 71 devices have been approved.

As we can see, most of these are for cancer radiology, which makes us correctly assume that it is for detecting cancer through various radiological scans. According to the researchers, of the approved devices, the vast majority (>80%) are related to the complicated area of cancer diagnostics.

The image above shows a deep learning algorithm trained to analyze MRI images and predict the presence of an IDH1 gene mutation in brain tumors.

Concerning different tumor types that AI-enhanced devices can investigate, most devices are being applied to a broad spectrum of solid malignancies defined as cancer in general (33.8%). However, the specific tumor that counts for the most significant number of AI devices is breast cancer (31.0%), followed by lung and prostate cancer (both 8.5%), colorectal cancer (7.0%), brain tumors (2.8%) and six other types (1.4% each).

Moving Forward with AI

From its origin, AI has shown its capabilities in nearly all scientific branches and continues to possess impressive future growth potential in oncology.

The devices that have already been approved are not conceived as a substitution for classical oncological analysis and diagnosis but as an integrative tool for exceptional cases and improving the management of cancer patients.

A cancer diagnosis has classically represented a starting point from which appropriate therapeutic and disease management approaches are designed. AI-based diagnosis is a step forward and will continue to be an essential focus in ongoing and future development. However, it will likely be expanded to other vital areas, such as drug discovery, drug delivery, therapy administration, and treatment follow-up strategies.

Current cancer types with a specific AI focus (breast, lung, and prostate cancer) are all high in incidence. This focus means that other tumor types have the opportunity for AI diagnosis and treatment improvements, including rare cancers that still lack standardized approaches.

However, rare cancers will take longer to create large and reliable data sets. When grouped, rare cancers are one of the essential categories in precision oncology, and this group will become a growing focus for AI.

With the positive results that have already been seen with AI in oncology, AI should be allowed to expand its reach and provide warranted solutions to cancer-related questions that it has the potential to resolve. If given this opportunity, AI could be harnessed to become the next step in a cancer treatment revolution.

Closing Thoughts

Artificial intelligence (AI) is reshaping many fields, including medicine and the entire landscape of oncology. AI brings to oncology several new opportunities for improving the management of cancer patients.

It has already proven its abilities in diagnosis, as seen by the number of devices in practice and approved by the FDA. The focus of AI has been on the cancers with the highest incidence, but rare cancers amount to a massive avenue of potential when grouped.

The next stage will be to create multidisciplinary platforms that use AI to fight all cancers, including rare tumors. We are at the beginning of the oncology AI revolution.

The Convergence of Technology and Healthcare

Posted on December 20, 2022December 20, 2022 by Jean Chalopin

We saw the changes to our lives with the Covid-19 pandemic playing the role of catalyst for changes in life sciences and healthcare. This article will discuss how new technologies, including blockchain, cybersecurity, and the needed talent behind these, are impacting the medical sector.

Recent Changes to Healthcare

We have seen how the past few years have been shaped by the Covid-19 pandemic, which disrupted and revolutionized nearly every sector of our economy.

When we look at monetary investment, it’s evident that technology spending is focused on healthcare. A report from Bain and Co found that even with economic uncertainty, healthcare is still planning to invest in tech, with software being a top five strategic priority for 80% of providers and a top three for 40%.

This spending is for several reasons: efficiency, cost reduction, and telemedicine, whether by phone or video. Heavy technology investment in the era of Covid-19 caused healthcare to leapfrog into patients’ homes.

These changes will be the driver of healthcare’s growth for the next few years. Yet we need to have a strong understanding of how the consumer fits into this system of delivering service, what their preferences are, and the new habits they are forming.

Once Before, in the 1920s

Periods of economic and geopolitical uncertainty have led to healthcare advancements.

In the 1920s, there were many geopolitical tensions that eventually led to wars, but throughout the decade and the rest of the 20^th century, there were remarkable advances in medicine.

The construction of hospitals that followed the passing of the Hill-Burton Act in 1946 made the foundation of our current health delivery system, the same way we saw our highway system and other infrastructure change the face of America and its economy. We’ll likely see a similar change around needed vaccines and other due innovations.

Rather than creating roads, bridges, and buildings, we’ll see digital infrastructure. Out of the discovery of the first mRNA Covid vaccines, we’ll find many ways to accelerate the process through biotechnology and innovation. Technology is an added dimension to healthcare innovation that has appeared out of the Covid turmoil. When technology is added to the mix, we’re going to see some fantastic opportunities.

The Covid Cause

It’s remarkable to think that a significant, globally impacting event is a catalyst that accelerates healthcare sector tech investment. If the necessary Covid closures were only for a single week, many of these changes would not have resulted.

Doctor visits would have been pushed back for that week instead of finding a remote solution that was needed to provide the required services and the resulting changed behaviors they have brought. The R&D plans that are now part of biotech and medical companies would likely not have manifested.

But we see that necessity is the mother of innovation, and because of Covid-19, these changes are incorporated and permanent. Many experts believe that the two years of Covid moved the industry ahead 5 to 10 years.

A Move Toward NFTs in Healthcare

Non-Fungible Tokens (NFTs) have been an investment darling in the art world but have yet to gain prominence much outside that and the collecting arenas. This lack of diversified uses is starting to change. Healthcare is up next.

NFTs are an exciting area for healthcare services. It’s easy to imagine a world where an NFT can become a patient’s profile in healthcare. An NFT profile has the capability to carry personal information such as the entire genome and all medical history and payment information as a unique footprint.

An NFT can also provide the owner with a pathway to get them into the healthcare system and provide them with services. This information can be combined with the banking system making their help more viable. Imagine a health saving account tied directly to the NFT through an oracle (a third-party gateway).

This will be able to allow someone to fund their health savings account through their W2-qualifying job. Charges that fit under the account can be automatically withdrawn.

This kind of payment system is just starting to happen on the municipal level. Cities like New York and Miami have begun to move toward such a system, with Philadelphia and Dearborn, Michigan, signaling similar moves. It’s not far-fetched to imagine a similar action to healthcare payments.

Cybersecurity in Healthcare

When there is human involvement, there is the potential for security vulnerabilities. The second issue that all companies are dealing with is finding the right talent that is capable of building systems and products able to protect company and personal data. There is an ongoing global shortage of nearly 3.5 million cybersecurity professionals across all industries, with 700,000 unfilled cybersecurity jobs in the US.

Cybersecurity for healthcare also requires the development of technicians that can play defense, quickly responding to cyberattacks in real-time. Hacking is accelerating and is a top risk profile for many companies, not just in tech.

Interestingly, one of hacking’s growing tools, AI, may also be its best solution as more information and services are digitized. Significant investment is happening in software projects that help protect and defend all data. In November 2022, Crunchbase showed 258 privacy startups that have raised over $4.3 billion, with $800 million of this total raised in the last year.

Life sciences and healthcare are industries that drive policies and security. Many boards and audit committees in the healthcare and life science sectors are attempting to identify various cyber risks and vulnerabilities. It’s fully expected that the demand for cyber-fluent personnel will increase dramatically.

Permanent Changes Coming to Healthcare

Tech is now taking over in several areas, including consumer electronics. Wearables and connected devices are becoming a more common source of medical information. Alivecor’s KardiaMobile device is a 6-lead EKG that can send information via smartphone directly to the patient’s cardiologist for review.

The Las Vegas consumer electronics show is filled with sensors, apps, and embedded personalization. This expansion of devices for our health will only increase as the 5G networks expand their reach across the United States. The impacts will be wide-ranging, but ultimately focus on enhancing our lives through tech.

One crucial, long-term benefit is that we are now seeing the healthcare economy moving from a sickness focus to a wellness mindset. This change is easier to accomplish with technology as we can monitor our health and see when things change.

Upcoming Healthcare Trends

The healthcare sector will first see a move toward modernization in human resources, finance, and procurement through cloud services. Moving all legacy enterprise systems to the cloud will take nearly ten years.

Next, innovation must tackle the back office to front office connection, including consumer-level devices. We have been discussing healthcare costs for decades, and the tech is now available to make it more efficient. This change can drive out costs and potentially deliver care to all.

Closing Thoughts

Technology in healthcare has been accelerated by Covid-19, pushing digital health access, and drug and vaccine innovation. These trends are altering research and development pathways for healthcare.

NFTs have begun to enter the healthcare space and, in the future, will likely be a secure way to provide needed information to providers, including genome and medical history. Cybersecurity issues will come to the forefront in healthcare tech with more need for talent and solutions to keep users’ data secure.

This need for talent will include the opportunity for tech to provide equitable solutions that lower costs and bring healthcare to all. A process of modernization that puts enterprise services on the cloud will be the biggest change we will see. Further, it will promote a focus of wellness over sickness as consumer devices become ubiquitous.

How AI Transforms Medical Research

Posted on October 17, 2022October 17, 2022 by Jean Chalopin

Using artificial intelligence (AI), businesses have been moving toward digital transformation long before the Covid-19 pandemic in their collective quest to optimize production, product quality, safety, services, and customer experiences. Some actively desired a sustainable planet for all.

The advantages of the next digital era feel limitless. Still, businesses are hesitant to adopt these technologies because they require significant behavioural and structural changes, such as new business models, operating procedures, worker skill sets, and mindsets. These technologies include not only AI, but machine learning, and deep learning “at the edge” (where rapid automation occurs).

The pandemic acted as a wake-up call to drastically accelerate the timescale for digital transformation since it put our way of life in danger.

The need is urgent and lifesaving, and the time is now. This is supported by a recent IBM poll that shows the Covid-19 epidemic caused the majority of global organizations (six out of 10) to accelerate their digital transformation strategies.

Source: https://www.globaldata.com/covid-19-accelerated-digital-transformation-timeline-pharmaceutical-industry/

Due to the pandemic, we can see how creative problem-solving and once-in-a-lifetime risk-taking leads to incredible breakthroughs and significant improvements.

Medical research is one vital area that is reaping the benefits of accelerated AI adoption.

AI and Predicting Outbreaks

Epidemiologists are already benefiting from the improvement of AI algorithms, which evaluate ever-increasing amounts of data made accessible to the public and track the onset and spread of infectious illnesses. To forecast the spread of the flu and other diseases in various regions, researchers are analysing geographical data and internet search inquiries on common symptoms.

Time is an advantage. Before calling a doctor, people are already aware that they are unwell. Before obtaining professional assistance, many people attempt to self-diagnose online.

Epidemiologists may use machine learning models to anticipate the spread of the flu in a particular location with a high degree of probability if they see a surge in searches for phrases like “sore throat” or “difficulty swallowing” originating from IP addresses in a specific ZIP code.

Source: https://time.com/5780683/coronavirus-ai/

Governmental health organizations assess crowd densities by location and analyse that information to forecast the likelihood of future outbreaks using public data and demographic mapping. For instance, to train machine learning models in indicating how many people would visit specific sites on a given day, health authorities in Europe, Israel, China, and other places utilize anonymized mobile phone traffic density data. Venues might limit attendance, reduce visiting hours, or even close if the total rises to pandemic levels.

Optimizing Treatment

AI is already being used to diagnose diseases earlier and with more accuracy, such as cancer. The American Cancer Society claims many mammograms provide misleading findings, telling one in two healthy women they have cancer. Mammogram reviews and translations are now 30 times faster and 99% accurate thanks to AI, eliminating the need for pointless biopsies.

People with chronic or lifelong diseases may perform better thanks to AI. One inspiring example: Machine learning models analyse cochlear implant sensor data to provide deaf patients feedback on how they sound so they can interact with the hearing world more effectively.

Computer Vision

In contrast to the human eye, AI-based computer vision can quickly sift through thousands of images to find patterns. In medical diagnostics, where overworked radiologists struggle to pick up every detail of one image after seeing hundreds of others, this technology is a great help. AI assists human specialists in situations like this by prioritizing visuals that are most likely to show a problem.

Source: https://www.altexsoft.com/blog/computer-vision-healthcare/

X-rays, CT scans, MRIs, ultrasound pictures, and other medical images provide a rich environment for creating AI-based tools that support clinicians with identifying various problems.

Drug Discovery

Small-molecule drug development can benefit from AI in four different ways: access to new biology, enhanced or unique chemistry, higher success rates, and speedier and less expensive discovery procedures. The solves numerous problems and limitations in conventional research and development. Each application gives drug research teams new information, and it might completely change tried-and-tested methods in certain situations.

Source: https://zitniklab.hms.harvard.edu/drugml/

AI is used by BioXcel Therapeutics to find and create novel drugs in the areas of neurology and immuno-oncology. The business’s drug re-innovation initiative also uses AI to uncover fresh uses for current medications or to locate new patients.

Transforming the Patient Experience

Time is money in the healthcare sector. Hospitals, clinics, and doctors treat more patients each day by effectively delivering a smooth patient experience.

In 2020, more than 33 million patients were admitted into U.S. hospitals, each with unique medical needs, insurance coverage, and circumstances that affected the quality of care. According to studies, hospitals with satisfied patients make more money, while those with dissatisfied patients may suffer financial losses.

New advancements are streamlining the patient experience in AI healthcare technologies, enabling medical personnel to handle millions, if not billions, of data points more effectively.

Employers who want to give their staff the tools to maintain good mental health can use Spring Health’s mental health benefits solution.

Each person’s whole dataset is collected as part of the clinically approved technology’s operation, and it is compared to hundreds of thousands of other data points. Using a machine learning approach, the software then matches users with the appropriate specialist for in-person care or telemedicine sessions.

For treating chronic illnesses like diabetes and high blood pressure, One Drop offers a discreet solution. With interactive coaching from real-world experts, predictive glucose readings powered by AI and data science, learning resources, and daily records taken from One Drop’s Bluetooth-enabled glucose reader, the One Drop Premium app empowers people to take control of their conditions.

AI Does Not Replace Humanity

Faster, more accurate diagnoses and lower claim processing error rates are just two of the potential benefits of AI that CEOs at healthcare organizations already see. But they must also realize that no amount of advanced technology will ever fully replace the human experience.

Business executives must also consider the possibility of bias in AI algorithms based on past beliefs and data sets, and put safeguards in place to address this problem. For instance, there has historically been discrimination in how specific populations’ medical illnesses are identified and treated.

AI is there to augment human decision-making in healthcare–not replace it.

Closing Thoughts

Traditionally, it’s tricky to understand whether AI is living up to its potential or whether everything we read is merely hype. For several years, due to the roadblocks outlined at the beginning of this article, progress has been slow and needed some hype. However, the pandemic is genuinely accelerating the integration of AI in healthcare and medical research. It almost sounds cliché now, but Covid-19 has initiated a “new normal” in healthcare.