Deep Medicine

How Artificial Intelligence Can Make Healthcare Human Again


By Eric Topol, MD

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A Science Friday pick for book of the year, 2019

One of America’s top doctors reveals how AI will empower physicians and revolutionize patient care

Medicine has become inhuman, to disastrous effect. The doctor-patient relationship–the heart of medicine–is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help. AI has the potential to transform everything doctors do, from notetaking and medical scans to diagnosis and treatment, greatly cutting down the cost of medicine and reducing human mortality. By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard.

Innovative, provocative, and hopeful, Deep Medicine shows us how the awesome power of AI can make medicine better, for all the humans involved.



Life can only be understood backwards; but it must be lived forwards.


AMONG THE MANY CHARACTERISTICS THAT MAKE US HUMAN and that distinguish us from other animals must be our urge to look back. It is hard to imagine that other species brood late at night about the one that got away or a job they could have had. But we also do it as a form of scholarship, looking back at ourselves as a species, as if we were the Creator, poring through recorded history, charting the milestones of progress, from the harnessing of fire to the microchip. Then we try to make sense of it.

Kierkegaard’s thesis that we live life forward but understand it backward might mean nothing more than we remember the past, and at best we have an (inaccurate) record of it. But with apologies to him and to George Santayana, understanding history does not provide immunity to repeating it. A cursory scan of the news shows this to be true. In short, even as a guide to what to avoid, the past is unreliable. Only the future is certain because it is still ours to make.

Which brings us to futurists, like the author of this wonderful book. Such individuals, on hearing that the Wright brothers became airborne, can foresee budget airlines, airline hubs, and humans walking on the moon. These historians of the now begin with the study of what is today, asking not how to avoid the perils of the past but how to maximize the advantages of the future. Pencil and paper, or tablet, in hand, they patrol the frontiers of science and tech and interview those at the cutting edge, including those who have tumbled over. They seek out innovators, scientists, mavericks, and dreamers. They listen, they monitor, they filter, and they synthesize knowledge across many disciplines to make sense of it all for the rest of us. As Deep Medicine will show you, theirs is a formidable intellectual task and an extraordinarily creative one. It involves as much right brain as left, and it invokes the muses, because what is in this book is as much inspiration as it is exposition.

Deep Medicine is Eric Topol’s third exploration of what will be. The previous books, examined in the light of where we are now, reveal his prescient vision. In Deep Medicine, Eric tells us we are living in the Fourth Industrial Age, a revolution so profound that it may not be enough to compare it to the invention of steam power, the railroads, electricity, mass production, or even the computer age in the magnitude of change it will bring. This Fourth Industrial Age, revolving around artificial intelligence (AI), robotics, and Big Data, heralds a profound revolution that is already visible in the way we live and work, perhaps even in the way we think of ourselves as humans. It has great potential to help, but also to harm, to exaggerate the profound gap that already exists between those who have much and those who have less each passing year.

This revolution will overtake every human endeavor, medicine not least among them. Medicine itself is at a moment of crisis. As a profession, for all the extraordinary advances in the art and science of medicine in the last four decades, we have too often failed our patients. We fail to follow proven guidelines, and we fail in the art by not seeing the unique person in front of us. We know their genome, but by not listening to their story, we don’t register their broken heart. We fail to see the neurofibroma that are raising lumps all over their skin, a finding that is relevant to their paroxysmal hypertension but that does need the gown to come off during the exam, does need our attention to be on the body and not on the screen; we miss the incarcerated hernia that explains an elderly patient’s vomiting and have to wait for an expensive CAT scan and a radiologist to tell us what was before our eyes. Countries with the biggest expenditures on healthcare lag behind those that spend much less in basic rankings such as infant mortality. I think it is very telling that Deep Medicine opens with a profound, personal, revealing anecdote of the author’s own painful and harrowing medical encounter that was a result of not being seen as an individual, someone with an uncommon disorder.

It should not surprise us that technology, despite the dramatic way it has altered our ability to image the body, to measure and monitor its molecular structure, can also fail just as badly as humans fail. The glaring example is in the electronic healthcare record systems (EHRs) currently in use in most hospitals. These EHRs were designed for billing, not for ease of use by physicians and nurses. They have affected physician well-being and are responsible for burnout and attrition; moreover, they have forced an inattentiveness to the patient by virtue of an intruder in the room: the screen that detracts from the person before us. In Intoxicated by My Illness, a poignant memoir about a man’s ultimately fatal prostate cancer, Anatole Broyard articulates a wish that his urologist would “brood on my situation for perhaps five minutes, that he would give me his whole mind just once, be bonded with me for a brief space, survey my soul as well as my flesh, to get at my illness, for each man is ill in his own way.1 This poignant declaration, from the era just before electronic medical records, expresses the fundamental need of a sick human being; it is timeless, I believe, resistant to change, even as the world around us changes. It bears emphasizing: each man and woman is ill in his or her own way.

I am excited about the future, about the power to harness Big Data. By their sheer capacity to plow through huge datasets and to learn as they go along, artificial intelligence and deep learning will bring tremendous precision to diagnosis and prognostication. This isn’t to say they will replace humans: what those technologies will provide is a recommendation, one that is perhaps more accurate than it has ever been, but it will take a savvy, caring, and attentive physician and healthcare team to tailor that recommendation to—and with—the individual seated before them. Over 2,000 years ago, Hippocrates said, “It is more important to know what sort of person has [a] disease than to know what sort of disease a person has.” In a 1981 editorial on using a computer to interpret risk after exercise stress testing, Robert Califf and Robert Rosati wrote, “Proper interpretation and use of computerized data will depend as much on wise doctors as any other source of data in the past.”2 This is a timeless principle, so long as it is humans we are discussing and not brake parts on an assembly line.

We come back in the end to the glorious fact that we are human, that we are embodied beings, a mind with all its complexities in a body that is equally complex. The interplay between one and the other remains deeply mysterious. What is not mysterious is this: when we are ill, we have a fundamental need to be cared for; disease infantilizes us, particularly when it is severe, and though we want the most advanced technical skills, scientific precision, the best therapy, and though we would want our physicians to “know” us (and unlike the time of Hippocrates, such knowing includes the genome, proteome, metabolome, transcriptome, predictions driven by AI, and so on), we badly want it to be expressed in the form of a caring, conscientious physician and healthcare team. We want the physician—a caring individual and not a machine—to give us time, to perform an attentive exam if for no other reason than to acknowledge the locus of disease on our body and not on a biopsy or an image or a report, to validate our personhood and our complaint by touching where it hurts. As Peabody said years ago, the secret of caring for patients is in caring for the patient.

We want those who care for us to know our hearts, our deepest fears, what we live for and would die for.

That is, and it always will be, our deepest desire.

Abraham Verghese, MD

Department of Medicine

Stanford University

chapter one


By these means we may hope to achieve not indeed a brave new world, no sort of perfectionist Utopia, but the more modest and much more desirable objective—a genuinely human society.



My wife and I looked at each other, bug-eyed, in total disbelief. After all, I hadn’t gone to my one-month post-op clinic visit following a total knee replacement seeking psychiatric advice.

My knees went bad when I was a teenager because of a rare condition known as osteochondritis dissecans. The cause of this disease remains unknown, but its effects are clear. By the time I was twenty years old and heading to medical school, I had already had dead bone sawed off and extensive reparative surgery in both knees. Over the next forty years, I had to progressively curtail my physical activities, eliminating running, tennis, hiking, and elliptical exercise. Even walking became painful, despite injections of steroids and synovial fluid directly into the knee. And so at age sixty-two I had my left knee replaced, one of the more than 800,000 Americans who have this surgery, the most common orthopedic operation. My orthopedist had deemed me a perfect candidate: I was fairly young, thin, and fit. He said the only significant downside was a 1 to 2 percent risk of infection. I was about to discover another.

After surgery I underwent the standard—and, as far as I was told, only—physical therapy protocol, which began the second day after surgery. The protocol is intense, calling for aggressive bending and extension to avoid scar formation in the joint. Unable to get meaningful flexion, I put a stationary bicycle seat up high and had to scream in agony to get through the first few pedal revolutions. The pain was well beyond the reach of oxycodone. A month later, the knee was purple, very swollen, profoundly stiff, and unbending. It hurt so bad that I couldn’t sleep more than an hour at a time, and I had frequent crying spells. Those were why my orthopedist recommended antidepressants. That seemed crazy enough. But the surgeon then recommended a more intensive protocol of physical therapy, despite the fact that each session was making me worse. I could barely walk out of the facility or get in my car to drive home. The horrible pain, swelling, and stiffness were unremitting. I became desperate for relief, trying everything from acupuncture, electro-acupuncture, cold laser, an electrical stimulation (TENS) device, topical ointments, and dietary supplements including curcumin, tart cherry, and many others—fully cognizant that none of these putative treatments have any published data to support their use.

Joining me in my search, at two months post-op, my wife discovered a book titled Arthrofibrosis. I had never heard the term, but it turned out to be what I was suffering from. Arthrofibrosis is a complication that occurs in 2 to 3 percent of patients after a knee replacement—that makes the condition uncommon, but still more common than the risk of infection that my orthopedist had warned me about. The first page of the book seemed to describe my situation perfectly: “Arthrofibrosis is a disaster,” it said. More specifically, arthrofibrosis is a vicious inflammation response to knee replacement, like a rejection of the artificial joint, that results in profound scarring. At my two-month post-op visit, I asked my orthopedist whether I had arthrofibrosis. He said absolutely, but there was little he could do for the first year following surgery—it was necessary to allow the inflammation to “burn out” before he could go back in and remove the scar tissue. The thought of going a year as I was or having another operation was making me feel even sicker.

Following a recommendation from a friend, I went to see a different physical therapist. Over the course of forty years, she had seen many patients with osteochondritis dissecans, and she knew that, for patients such as me, the routine therapeutic protocol was the worst thing possible. Where the standard protocol called for extensive, forced manipulation to maximize the knee flexion and extension (which was paradoxically stimulating more scar formation), her approach was to go gently: she had me stop all the weights and exercises and use anti-inflammatory medications. She handwrote a page of instructions and texted me every other day to ask how “our knee” was doing. Rescued, I was quickly on the road to recovery. Now, years later, I still have to wrap my knee every day to deal with its poor healing. So much of this torment could have been prevented.

As we’ll see in this book, artificial intelligence (AI) could have predicted that my experience after the surgery would be complicated. A full literature review, provided that experienced physical therapists such as the woman I eventually found shared their data, might well have indicated that I needed a special, bespoke PT protocol. It wouldn’t only be physicians who would get a better awareness of the risks confronting their patients. A virtual medical assistant, residing in my smartphone or my bedroom, could warn me, the patient, directly of the high risk of arthrofibrosis that a standard course of physical therapy posed. And it could even tell me where I could go to get gentle rehab and avoid this dreadful problem. As it was, I was blindsided, and my orthopedist hadn’t even taken my history of osteochondritis dissecans into account when discussing the risk of surgery, even though he later acknowledged that it had, in fact, played a pivotal role in the serious problems that I encountered.

Much of what’s wrong with healthcare won’t be fixed by advanced technology, algorithms, or machines. The robotic response of my doctor to my distress exemplifies the deficient component of care. Sure, the operation was done expertly, but that’s only the technical component. The idea that I should take medication for depression exemplifies a profound lack of human connection and empathy in medicine today. Of course, I was emotionally depressed, but depression wasn’t the problem at all: the problem was that I was in severe pain and had Tin Man immobility. The orthopedist’s lack of compassion was palpable: in all the months after the surgery, he never contacted me once to see how I was getting along. The physical therapist not only had the medical knowledge and experience to match my condition, but she really cared about me. It’s no wonder that we have an opioid epidemic when it’s a lot quicker and easier for doctors to prescribe narcotics than to listen to and understand patients.

Almost anyone with chronic medical conditions has been “roughed up” like I was—it happens all too frequently. I’m fortunate to be inside the medical system, but, as you have seen, the problem is so pervasive that even insider knowledge isn’t necessarily enough to guarantee good care. Artificial intelligence alone is not going to solve this problem on its own. We need humans to kick in. As machines get smarter and take on suitable tasks, humans might actually find it easier to be more humane.

AI in medicine isn’t just a futuristic premise. The power of AI is already being harnessed to help save lives. My close friend, Dr. Stephen Kingsmore, is a medical geneticist who heads up a pioneering program at the Rady Children’s Hospital in San Diego. Recently, he and his team were awarded a Guinness World Record for taking a sample of blood to a fully sequenced and interpreted genome in only 19.5 hours.1

A little while back, a healthy newborn boy, breastfeeding well, went home on his third day of life. But, on his eighth day, his mother brought him to Rady’s emergency room. He was having constant seizures, known as status epilepticus. There was no sign of infection. A CT scan of his brain was normal; an electroencephalogram just showed the electrical signature of unending seizures. Numerous potent drugs failed to reduce the seizures; in fact, they were getting even more pronounced. The infant’s prognosis, including both brain damage and death, was bleak.

A blood sample was sent to Rady’s Genomic Institute for a rapid whole-genome sequencing. The sequence encompassed 125 gigabytes of data, including nearly 5 million locations where the child’s genome differed from the most common one. It took twenty seconds for a form of AI called natural-language processing to ingest the boy’s electronic medical record and determine eighty-eight phenotype features (almost twenty times more than the doctors had summarized in their problem list). Machine-learning algorithms quickly sifted the approximately 5 million genetic variants to find the roughly 700,000 rare ones. Of those, 962 are known to cause diseases. Combining that information with the boy’s phenotypic data, the system identified one, in a gene called ALDH7A1, as the most likely culprit. The variant is very rare, occurring in less than 0.01 percent of the population, and causes a metabolic defect that leads to seizures. Fortunately, its effects can be overridden by dietary supplementation with vitamin B6 and arginine, an amino acid, along with restricting lysine, a second amino acid. With those changes to his diet made, the boy’s seizures abruptly ended, and he was discharged home thirty-six hours later! In follow-up, he is perfectly healthy with no sign of brain damage or developmental delay.

The key to saving this boy’s life was determining the root cause of his condition. Few hospitals in the world today are sequencing the genomes of sick newborns and employing artificial intelligence to make everything known about the patient and genomics work together. Although very experienced physicians might eventually have hit upon the right course of treatment, machines can do this kind of work far quicker and better than people.

So, even now, the combined efforts and talents of humans and AI, working synergistically, can yield a medical triumph. Before we get too sanguine about AI’s potential, however, let’s turn to a recent experience with one of my patients.

“I want to have the procedure,” my patient told me on a call after a recent visit.

A white-haired, blue-eyed septuagenarian who had run multiple companies, he was suffering from a rare and severe lung condition known as idiopathic—a fancy medical word for “of unknown cause”—pulmonary fibrosis. It was bad enough that he and his pulmonologist had been considering a possible lung transplant if it got any worse. Against this backdrop he began to suffer a new symptom: early-onset fatigue that left him unable to walk more than a block or swim a lap. He had seen his lung doctor and had undergone pulmonary function tests, which were unchanged. That strongly suggested his lungs weren’t the culprit.

He, along with his wife, then came to see me, very worried and depressed. He took labored, short steps into the exam room. I was struck by his paleness and look of hopelessness. His wife corroborated his description of his symptoms: there had been a marked diminution of his ability to get around, to even do his daily activities, let alone to exert himself.

After reviewing his history and exam, I raised the possibility that he might have heart disease. A few years previously, after he began to suffer calf pain while walking, he had stenting of a blockage in his iliac artery to the left leg. This earlier condition raised my concern about a cholesterol buildup in a coronary artery, even though he had no risk factors for heart disease besides his age and sex, so I ordered a CT scan with dye to map out his arteries. The right coronary artery showed an 80 percent narrowing, but the other two arteries were free of significant disease. It didn’t fit together. The right coronary artery doesn’t supply very much of the heart muscle, and, in my thirty years as a cardiologist (twenty of which involved opening coronary arteries), I couldn’t think of any patients with such severe fatigue who had narrowing in only the right coronary artery.

I explained to him and to his wife that I really couldn’t connect the dots, and that it might be the case of a “true-true, unrelated”—that the artery’s condition might have nothing to do with the fatigue. His underlying serious lung condition, however, made it conceivable that the narrowing was playing a role. Unfortunately, his lung condition also increased the risk of treatment.

I left the decision to him. He thought about it for a few days and decided to go for stenting his right coronary artery. I was a bit surprised, since over the years he had been so averse to any procedures and even medications. Remarkably, he felt energized right after the procedure was done. Because the stent was put in via the artery of his wrist, he went home just a few hours later. By that evening, he had walked several blocks and before the week’s end he was swimming multiple laps. He told me he felt stronger and better than he had for several years. And, months later, the striking improvement in exercise capacity endured.

What’s remarkable about this story is that a computer algorithm would have missed it. For all the hype about the use of AI to improve healthcare, had it been applied to this patient’s data and the complete corpus of medical literature, it would have concluded not to do the procedure because there’s no evidence that indicates the opening of a right coronary artery will alleviate symptoms of fatigue—and AI is capable of learning what to do only by examining existing evidence. And insurance companies using algorithms certainly would have denied reimbursement for the procedure.

But the patient manifested dramatic, sustained benefit. Was this a placebo response? That seems quite unlikely—I’ve known this man for many years, and he tends to minimize any change, positive or negative, in his health status. He seems a bit like a Larry David personality with curbed enthusiasm, something of a curmudgeon. Ostensibly, he would be the last person to exhibit a highly exaggerated placebo benefit.

In retrospect, the explanation likely does have something to do with his severe lung disease. Pulmonary fibrosis results in high pressures in the pulmonary arteries, which feed blood to the lungs, where the blood becomes oxygenated. The right ventricle is responsible for pumping that blood to the heart; the high blood pressure in the arteries meant that it would have taken a lot of work to force more blood in. That would have stressed the right ventricle; the stent in the right coronary artery, which supplies the right ventricle, would have alleviated the stress on this heart chamber. Such a complex interaction of one person’s heart blood supply with a rare lung disease had no precedent in the medical literature.

This case reminds us that we’re each a one-of-a-kind intricacy that will never be fully deconvoluted by machines. The case also highlights the human side of medicine: We physicians have long known that patients know their body and that we need to listen to them. Algorithms are cold, inhumane predictive tools that will never know a human being. Ultimately, this gentleman had a sense that his artery narrowing was the culprit for his symptoms, and he was right. I was skeptical and would certainly not have envisioned the magnitude of impact, but I was thrilled he improved.

AI HAS BEEN sneaking into our lives. It is already pervasive in our daily experiences, ranging from autocomplete when we type, to unsolicited recommendations based on Google searches, to music suggestions based on our listening history, to Alexa answering questions or turning out the lights. Conceptually, its roots date back more than eighty years, and its name was coined in the 1950s, but only recently has its potential impact in healthcare garnered notice. The promise of artificial intelligence in medicine is to provide composite, panoramic views of individuals’ medical data; to improve decision making; to avoid errors such as misdiagnosis and unnecessary procedures; to help in the ordering and interpretation of appropriate tests; and to recommend treatment. Underlying all of this is data. We’re well into the era of Big Data now: the world produces zettabytes (sextillion bytes, or enough data to fill roughly a trillion smartphones) of data each year. For medicine, big datasets take the form of whole-genome sequences, high-resolution images, and continuous output from wearable sensors. While the data keeps pouring out, we’ve really processed only a tiny fraction of it. Most estimates are less than 5 percent, if that much. In a sense, it was all dressed up with nowhere to go—until now. Advances in artificial intelligence are taming the unbridled amalgamation of Big Data by putting it to work.

There are many subtypes of AI. Traditionally machine learning included logistic regression, Bayesian networks, Random Forests, support vector machines, expert systems, and many other tools for data analysis. For example, a Bayesian network is a model that provides probabilities. If I had a person’s symptoms, for example, such a model could yield a list of possible diagnoses, with the probability of each one. Funny that in the 1990s, when we did classification and regression trees to let the data that we collected speak for itself, go into “auto-analyze” mode, without our bias of interpretation, we didn’t use the term “machine learning.” But now that form of statistics has undergone a major upgrade and achieved venerability. In recent years, AI tools have expanded to deep network models such as deep learning and reinforcement learning (we’ll get into more depth in Chapter 4).

The AI subtype of deep learning has gained extraordinary momentum since 2012, when a now-classic paper was published on image recognition.2

FIGURE 1.1: The increase in deep learning AI algorithms since the 2012 image recognition paper. Sources: Panel A adapted from A. Mislove, “To Understand Digital Advertising, Study Its Algorithms,” Economist (2018): Panel B adapted from C. Mims, “Should Artificial Intelligence Copy the Human Brain?” Wall Street Journal (2018):

FIGURE 1.2: The exponential growth in computing—300,000-fold—in the largest AI training runs. Source: Adapted from D. Hernandez and D. Amodei, “AI and Compute,” OpenAI (2018):

The number of new deep learning AI algorithms and publications has exploded (Figure 1.1), with exponential growth of machine recognition of patterns from enormous datasets. The 300,000-fold increase in petaflops (computing speed equal to one thousand million million [1015] floating-point operations per second) per day of computing used in AI training further reflects the change since 2012 (Figure 1.2).


  • "Dr. Topol's vision of medicine's future is optimistic. He thinks set to save time, lives and money."—The Economist
  • "Topol passionately and persuasively sets out the transformational potential of deep medicine."—Lancet
  • "[Topol's] argument for using technology to bring care back to health care is timeless."—Nature
  • "An optimistic vision of medicine's rapidly approaching future that should be required reading for the public and medical people alike."—Booklist
  • "Enlightening... Anyone with an avid curiosity about the future of medicine will find this worthwhile."—Publishers Weekly
  • "A gimlet-eyed look at the role of computers in medicine...A cogent argument for a more humane -- and human -- medicine, assisted by technology but not driven by it."—Kirkus
  • "Eric Topol has a unique knack for bringing us to the frontiers of medicine in his books, and this one is no exception. A compulsively readable, elegantly written, important account, Deep Medicine will fundamentally change the way you view the future of medical technologies and their impact on our lives. This book is challenging, thoughtful, and provocative. I cannot recommend it enough."—Siddhartha Mukherjee, author of The Emperor of All Maladies
  • "Healthcare offers the best opportunity for symbiotic combination of artificial intelligence and humanity. Eric Topol's book is the definitive work from someone who deeply understands both healthcare and AI. I strongly recommend the book, and hope it connects medical practitioners and AI researchers, and help them understand that only by working together, can our shared dreams of health and longevity be reached."—Kai-Fu Lee, bestselling author of AI Superpowers
  • "Deep Medicine is a fascinating tour of how machine learning is transforming medical research, with medical care on the horizon. Topol reminds us that as our machines get smarter and capable of taking over more of our tasks, we must become more human, and more humane, to compensate. Our most brilliant AI tools will help us learn more about ourselves--body and mind--than we can even imagine, but they cannot empathize with a patient. This book is an excellent step toward directing all that knowledge into creating a healthier society, not just healthier individuals."—Garry Kasparov, author of Deep Thinking
  • "The promise of Artificial Intelligence is deeply human, and its impact is only growing in industry and daily life alike. Deep Medicine is an insightful read about the incredible potential of AI and medicine, written from a refreshingly human-centered perspective. It's not only a landmark book, but the start of a truly historic conversation about the implications of this exciting technology in medicine."—Fei-Fei Li, professor of computer science at Stanford University and director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab
  • "Deep Medicine is an essential look at the future of AI-powered healthcare, told by one of the most exciting researchers in the field."—Andrew Ng, General Partner at AI Fund and CEO of Landing AI

On Sale
Mar 12, 2019
Page Count
400 pages
Basic Books

Eric Topol, MD

About the Author

Eric Topol, MD, is a world-renowned cardiologist, Executive Vice-President of Scripps Research, founder of a new medical school and one of the top ten most cited medical researchers. The author of The Patient Will See You Now and The Creative Destruction of Medicine, he lives in La Jolla, CA.

Learn more about this author