Over the past 5 years, I have watched the hype and disillusion around the potential use of artificial intelligence in healthcare. Much of the early hype was driven by IBM Watson and its purported singular capability to help providers and researchers discover new ways to detect and treat disease, especially cancer. The initial promise later became a trail of disillusion when the technology did not initially provide the breakthroughs promised and often was harder to use than advertised. Fortunately, we now appear to be entering a time where there is some level setting and much more realistic thinking regarding AI and healthcare.
AI healthcare applications are beginning to show up more, not only aimed at providers and researchers, but also to help deal with backend challenges that health plans and others face with areas such as call centers, systems management, security, and financial integrity. There is also an increasing recognition that AI is not a short-term fix but something whose value will continue to evolve and grow in the coming years.
Recently Eric Topol, a California cardiologist who has written several well-received books on health IT applications, published Deep Medicine, which discusses healthcare and artificial intelligence. His book provides an excellent summary of the history of AI in healthcare and the various ways it is beginning to be used and add value, mostly from the provider and researcher perspective. Topol, because of his clinical background and knowledge, can describe in a fair amount of detail how AI will change the world of medicine. While careful not to oversell the technology, he provides a balanced view that describes how AI can play a key role in improving a providers’ clinical skills and, in some cases, take over some of the functions traditionally performed by clinicians. His discussion on the future of radiology, for example, does a great job of describing the current challenges that radiologists face and where the application of AI will lead to better and more effective diagnosis and treatment.
Topol focuses on what he calls the “three D’s” where AI can make a major difference: more deeply define an individual’s “medical essence” based all relevant data; deep learning for a wide variety of applications to provide more diagnostic and other support for the physician, the hospital, the staff, and the patient; finally, deep empathy where he sees AI freeing the physician to better connect with the patient on a more humanistic level.
The challenges that Topol describes and that other recent authors, including Amy Webb in The Big Nine, have echoed, is that AI can go in many different directions, some of which can help healthcare, and other ways where AI can create harm either through biased algorithms, privacy intrusions, or introducing new security or safety issues.
I think that what is needed is an area that Topol does not discuss: a clearly defined vision for AI’s various roles in healthcare and then developing the governance and ecosystem changes required to help get us there. Achieving this framework not only requires public-private partnerships but also better cooperation between nation-states, especially the US and China, where the biggest AI investments are occurring.
Even with its many challenges, AI will probably be the most exciting technology development to impact healthcare in the coming years. Its benefits will touch all of us in ways that we can’t fully anticipate today.