American physician and surgeon William J. Mayo, one of the founders of the famed Mayo Clinic, stated, “The aim of medicine is to prevent disease and prolong life, the ideal of medicine is to eliminate the need of a physician.” Emerging applications of artificial…
American physician and surgeon William J. Mayo, one of the founders of the famed Mayo Clinic, stated, “The aim of medicine is to prevent disease and prolong life, the ideal of medicine is to eliminate the need of a physician.” Emerging applications of artificial intelligence (AI) , as well as medical research trends, suggests that we are moving toward fulfilling medicine’s aim and achieving its ideal.
Health care organizations appear to be preparing themselves for the next technological step. For instance, in 2014 health care providers spent 4.2 percent of their revenues on IT, compared to a 3.3 percent cross-industry average. Penetration of electronic health care records grew from 40 percent in 2012 to 67 percent in 2017. With its wealth of smart machines, health care is expected to be among the fastest growing industries in terms of data generated. Cisco estimates a 2015-2020 CAGR (compound annual growth rate) of machine-to-machine connections in health care to be 30 percent, more than the expected 29 percent growth rate for connected cars.
The next big thing in health care is also anticipated by investors, who have increased their bets on the segment. Venture capital investment in cutting-edge, AI-driven medical technologies like computer vision, machine learning (ML) , and robotics has skyrocketed from $30 million in 2012 to $892 million in 2016.
Studying the academic and funding dimensions of the medical AI ecosystem, we see that the movement towards Mayo’s vision is taking place. Prediction and prevention, wellness and rehabilitation, amelioration of aging, and technological augmentation of doctors are all noticeable themes.
Prediction and prevention are well-known concepts for health care professionals. Now they appear to be revitalized and reinforced by machine learning. A dive into PubMed databases demonstrates that the pace of research activity for ML-powered prediction and prevention is currently higher than the research activity associated with these concepts without involving ML.
AI health care startups working with predictive and preventive medicine are a new phenomenon that seems to embrace growing research. Out of 218 health care AI startups selected from an industry database, 54 were involved in predictive medicine, with 44 founded in 2010 or later.
Some companies, like Jvion and HBI Solutions, provide health care organizations with patient-level predictions and risk scores. Others, like Ocuvera, bring prevention to hospitals’ operations by, for example, by identifying a patient’s proclivity to fall and helping to avoid the accident.
Activity in the wellness segment of the health care value chain also reflects growing interest in the preventive aspect of medicine. Wellness appears to be the fastest-growing segment among the core segments of the health care value chain.
Researchers’ attention to the wellness segment is matched by entrepreneurs’ interest. Out of 218 AI health care startups, 21 develop wellness applications.
Startup funding data suggests that younger startups tend to work with wellness applications. About 95 percent of AI-powered wellness startups were founded in 2010 or later, compared with 57 percent of those tackling surgery-related issues.
Wellness applications may use almost unlimited data from healthy populations, the collection of which is accelerated by new devices entering the market. The more data from healthy patients is available, the more insight one can get. Traditional health care uses data that is limited by the number of cases and more severe sampling requirements.
Prevention and prediction segments start from research into cells and genetics, aiming to eliminate the underlying causes of dangerous diseases. Machine learning drives these research topics as well.
Founding data for AI startups helps to identify the uptick in launching startups working with cell and genetic research. For example, notable companies such as Human Longevity, BenevolentAI, Recursion Pharmaceuticals, and at least seven others were launched between 2010 and 2017.
Following Mayo’s vision, health care researchers and founders try to make life longer by battling aging and making rehabilitation smoother. Medical research on aging is growing rapidly, compared with research on the leading causes of death in the U. S. It is also one of the fastest accelerating research area in the past six years.
Not being a disease in the traditional sense, aging is an excellent target for tech disruption, with no critical state (i.e., the fast deterioration of a patient) and developing across the whole lifespan. Aging research may benefit from lots of data collected during a patient’s life.
Already amassed data and evolving data collection technologies, combined with machine learning, contribute to fighting aging. For instance, this technology can check if senescence acceleration is taking place and estimate biological age more precisely. Then relevant treatment options can be selected. BioageLabs and Insilico Medicine use machine learning to discover anti-aging drugs.
Rehabilitation is experiencing a growing research interest as well. It also benefits from AI as it requires long-term commitment, repetitive actions, and a continuous feedback loop. Twelve startups are moving the field of rehabilitation forward by, for instance, working with brain dysfunction rehabilitation like Intendu or helping joint replacement patients like Peerwell.
The examples given above suggest that tech is moving medicine toward preventing diseases from happening. This can be done by tweaking genes, detecting early signs of diseases, and altering human behavior for health benefits. Currently AI tech penetrates just a part of the list of dangerous diseases.
Perhaps at some point, all diseases will be preventable and there will be no need for a physician. But can tech eliminate physicians before it eliminates diseases by replacing human doctors with robots and algorithms? Our observation suggests that this goal is not a heavy area of interest.
