A Deep Look At The Future Of Medicine
Deep Medicine: How Artificial Intelligence and Make Healthcare Human Again
New York: Basic Books, 2019
By Mary Chitty, Library Director & Taxonomist, Cambridge Healthtech Institute
February 26, 2020 Cardiologist Eric Topol, author of the Creative Destruction of Medicine and The Patient Will See You Now has often written about medicine and technology. But Deep Medicine isn’t just about technology.
“Much of what’s wrong with healthcare won’t be fixed by advanced technology, algorithms, or machines…Artificial intelligence alone isn’t going to solve this problem on its own. We need humans to kick in. As machines get smarter and take on suitable tasks, human might actually find it easier to be more humane,” Topol writes.
Editor's Note: Eric Topol will be giving the plenary address at next week's Molecular Medicine TriConference in San Francisco. Topol will be speaking on Monday, March 2, in the 4:35 plenary session. Complimentary passes to see the talk are available now.
Topol emphasizes that medicine is still at the very early stages of utilizing and integrating computers and electronics. Electronic Health Records are not yet widely compatible or user friendly, are often incomplete and inaccurate, with much material copied and pasted, perpetuating mistakes and lacking useful summaries. Physicians spend an inordinate amount of time typing and looking at their computer screens. Overuse of screening leads to false positives and unnecessary procedures, all contributing to the growing costs of US healthcare.
Topol defines “deep medicine” as deeply defining individuals using layers of data including DNA, RNA, proteins, metabolites, immunome, microbiome, epigenome and more. This is sometimes called deep phenotyping or deep learning—not just pattern recognition and machine learning, but also involving medical coaching, machine vision and telehealth—and “deep empathy and connection between patients and clinicians.” He argues for focusing on AI and machine learning to do what machines are best at: consistent repetitive tasks and identification of underlying patterns, thereby freeing up time for more and better communication between clinicians and patients.
Topol begins with looking at the impact of deep learning on medical specialties such as pathology, radiology, and dermatology. He also acknowledges the necessity of examining artificial intelligence’s liabilities such as bias, healthcare inequities, privacy, security and the black box nature of AI.
Highly recommended for everyone concerned about the future of healthcare and readers who appreciated books such as Jerome Groopman’s How Doctors Think and Your Medical Mind: How To Decide What Is Right For You.