The New York Times Book Review - Rachel Riederer
Topol's argument isn't that human doctors should or will be replaced by A.I., but that there are different fields and tasks within medicine that are best approached by one or the other, or the two working together. He envisions a future where doctors and machines work symbiotically…Topol writes most effectively from his perspective as a physician, detailing actual cases and extending his caretaking sensibility to the reader.
From the Publisher
"Dr. Topol's vision of medicine's future is optimistic. He thinks AI...is 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
Kirkus Reviews
2019-01-13
A gimlet-eyed look at the role of computers in medicine.
Building on earlier fly-on-the-wall looks at modern healing (The Patient Will See You Now: The Future of Medicine Is in Your Hands, 2015, etc.), cardiologist Topol examines the pros and cons of putting artificial intelligence, database crunching, and the like into the service of doctors who may or may not appreciate the new powers gained and limits reached. In this, the question is one of building a body of testable data and using it wisely. As the author writes, "shallow evidence…leads to shallow medical practice, with plenty of misdiagnosis and unnecessary procedures." The data is more abundant than the meaning derived from it—by most estimates, Topol writes, doctors have collectively absorbed perhaps 5 percent of the whole literature. AI is useful for plowing through that huge body of material and weeding out the inapplicable and unlikely. AI is not, however, yet up to the "outlandish expectations," as he puts it, that some administrators—and, more to the point, cost-cutting insurers—are placing on it, from curing cancer to eliminating possible harm to patients to lessening workloads. To be sure, he notes, there are many places where an algorithm's ability to "eat data" is most welcome, as with correlating a patient's intake of fluids with his or her output of urine. Given that most Americans have their medical records scattered over many providers and insurers, it's important that data be consolidated and put in the hands of consumers. Perhaps paradoxically, notes the author, "the only way it can be made secure is to be decentralized." Another issue is the possible overreliance of doctors on data in the place of good practice, and Topol closes with the warning: "Machine medicine need not be our future."
A cogent argument for a more humane—and human—medicine, assisted by technology but not driven by it.
