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.
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.
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.
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.
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.
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.
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.
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.
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.
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.deltecbank.com.
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.deltecbank.com.
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.