Early Indications April 2019: Review essay: Deep Medicine by Eric Topol

Note: I neglected last month to announce that MIT Press has published my book on 3D printing,  and that an article on some managerial implications of same came out in the Journal of  Organizational Design.  ***** Eric Topol is a cardiologist who is very visible on the front lines of the medical field as it intersects  with smartphones, distributed sensors, and other emerging technologies. His most recent book  looks at "how artificial intelligence can make healthcare human again," in the words of the subtitle.  While I have plenty of honest disagreements with Topol's book, I very much respect it, and hope  that it jump-starts some way-overdue discussions about the future of medicine, particularly in the US. Given that I don't work in medicine, I'm extrapolating my lay experience here. The phrase  "personalized medicine" conjures up, for me at least, micro-targeted pharmaceuticals: my dose of  heart, anti-depressant, or chemotherapy medication will be fit precisely to my weight, age,  metabolism, and holistic health picture. Topol makes it clear that I am off base here. First, time  -- of day, of season, of life -- matters a lot: precision dosing entails understanding time-series  data in entirely new ways.  Second, precision medicine may likely begin not with pharmaceuticals but with diet. Recent  research on the role of gut flora and related participants in digestion helps explain why nutritional  research lacks any consistent consensus: meat was good, then bad, now it's sometimes good.  Butter was normal, then evil, but now it's not nearly as bad as margarine. Sugar was portrayed as  benign (so was cigarette smoking, for the same reasons), but now the "a calorie is just a calorie"  fiction is being exposed. One reason that dietary advice is so unreliable is that the degree of  uniqueness in our digestion and metabolization patterns is only now coming into focus. A key factor in that biomedical research into the gut biome and related processes is a massive  increase in the size of the data sets being researched. Much as with Facebook photos or Google  searches, machine learning algorithms need enormous volumes of training data to become reliable.  If for no other reason, Topol's book is valuable for chapter 11, where he explains the rapidly  changing literature of diet and digestion research. One point is made quite emphatically in the book, though I don't recall the author saying so directly.  Given the vast number of AI-infused startups for medical applications, we are nowhere remotely  close to general AI: every company he discusses, some of them in depth and/or with great  enthusiasm, is a point solution. Diabetes-related readings are the specialty of one company, while  mammograms are graded somewhere else, and retinopathy is diagnosed somewhere else. In short,  AI will not replace doctors anytime soon. This is not to say, however, that physicians' jobs won't change. Certain tasks -- image recognition  in particular -- are done quite capably by machines. Note, however, that image recognition is not  disease diagnosis (aided by up-to-date knowledge of a vast literature), treatment planning, end-of- life counseling, arguing with insurance companies, or any of the dozens of other things doctors are  called upon to do. The challenge ahead will be to team doctors and computers in the most effective  possible proportions, letting each contributor provide inputs that will lead to the whole patient  experience amounting to more than the sum of its parts. Topol also provides useful context for the role of AI in employment: there are plenty of subspecialties  in the US that are far understaffed, and globally, many nations have staggeringly few doctors. AI  provides the quite realistic possibility, in dermatology care for example, of enhancing family doctors  and nurse practitioners as they address one condition among many they see in a routine workweek.  Up-skilling generalists is entirely different from threatening the livelihoods of the relatively small  number of specialists called upon to address the major issue of deadly melanomas in the U.S.  and elsewhere.  Compared to a Watson-like digestion of medical research (something that seems plausible, if not  yet realized), for machine learning algorithms to read patient records remains a daunting task. Even  with electronic medical records, the lack of standardization in people's lives, descriptions of their  symptoms, health professionals' acuity in observation and notation, interoperability between  institutions (despite potentially using the same software package), and, again, the time of  observation means that mining EMRs is for practical purposes impossible for the foreseeable  future. In the meantime, EMRs are linked to physician burnout and decreased patient satisfaction  -- doctors navigating screens and typing with their backs to the patient in the exam room are  hugely troublesome for all concerned. Speech recognition powered by machine learning could  help alleviate this issue, and Topol ends the book by imagining a "super-Siri" personal health  assistant that helps each of us live our best life, medically speaking, through an advanced form  of what the military calls data fusion. For all the many drawbacks of current EMRs and other technologies, however, it's time to  address the elephant in the room: managed care. To the extent that my genomic, diagnostic,  behavioral, and other data becomes a tool for prediction of my health (and by extension, the  cost to maintain it), there will be a fundamental conflict of interest between me and my insurer.  Topol nods at this issue but never engages it (he is, however, a consultant to some of those  insurers). As the old saying goes, "follow the money," so my conviction is that the initial large- scale deployments of AI in health care will not be to reduce diabetic retinitis or arthrofibrosis,  but rather in a reimbursement arms race between insurers and providers. It's entirely possibly  that both Aetna and a health network will have deployed IBM Watson systems as the two  parties dispute a billed transaction, so we may see algorithms at war with themselves. It seems far fetched, but perhaps the EMR + AI convergence will be the beginning of the end  of the U.S.'s unique model of health care, in which more is spent per patient than anywhere  in the world while most major outcomes rank well down the list. That is, the fundamental conflict  of interest noted above will lead to such complex statutes, regulations, and/or case law that the  current U.S. arrangements become unsustainable. Already far too much effort is expended in  paperwork that does nothing for patient care, and the implications of predictive algorithm-driven  medicine for privacy, doctor-patient confidentiality, and ethics make HIPAA look positively  antiquated and inadequate.  If Topol can envision a new "virtual medical coach," perhaps the American Medical Association  can help construct a 21st-century alternative to its previous objections to single-payer health  care. Such a design for medicine would acknowledge the new roles of genomic science, data  analytics, robotics, and machine learning, along with addressing end-of-life, opioid, and other  political hot-button issues. I'm not convinced AI can give physicians time and tools to become  "human again" (beware anyone selling a golden age fallacy), but medicine absolutely needs  to become individualized, beginning from a data foundation, for the first time.