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.
Simona Mellino a Life Sciences Senior Consultant at Accenture in Basel lists what she liked most about the book:
The book is easy to follow, even for those without an extensive background in machine learning. Topol takes the time to provide the readers with basic definitions of machine learning and AI. This makes the book easy to read for someone in the healthcare sector, even without any specific knowledge of technology. Furthermore, in his chapter “Deep Liabilities”, he explores the “potentially adverse issues” confronting deep medicine, including biases, job concerns, and ethical issues which provide some context of AI applications in real life.
Deep Medicine covers a huge variety of examples and references. Topol did an impressive job in pulling together solid literature from the past years in each of his chapters. In fact, he provides not only examples of AI applications, but also data from US clinical practices, including misdiagnosis figures, unnecessary medical tests, and more. This comes from a review of medical literature as well as his own experience.One striking example is the Nature review of 2015, “Precision Medicine: Time for one-person trials”, showing failure rates of top medications in the market. These references clearly communicate the urge for improvement in the healthcare sector and motive the reader to keep exploring the presented content with high interest.
Topol uses a lot of examples from his life, which I find particularly interesting. In the chapter titled “Deep Diet”, he reviews start-ups in the field of personalized nutrition. He also shows his own gut microbiome assessment taken by an Israeli company. He concludes the chapter by providing an overview of the limitations in personalized nutrition and ties them back to his own experience. This makes the book very real, and as a reader it made me feel closer to the author.
Perhaps the part that I liked most is the final one, where Topol invites doctors to use the time freed up by technology to get to know their patients better and build a relationship with them. Topol describes the meaning of what “shallow” medicine is, a medicine where doctors do not have sufficient time to spend with their patients and rather focus on procedures: “This is where we are today: patients exist in a world of insufficient data, insufficient time, insufficient context, and insufficient presence. Or, as I say, a world of shallow medicine.”
Simona also lists what she liked least about the book:
As mentioned above, the book provides an extensive overview of medical and technology-related literature. In some chapters, I failed to really understand the “so what?” of the author. At times, I felt lost in the myriad of examples provided and I asked myself what Topol’s conclusion and recommendation moving forward really was. It is obviously difficult to make bold statements given the novelty of the field.
I would have loved to see a conclusive chapter covering current policies and regulatory hurdles for the development and deployment of novel technologies. While the book perfectly expresses the need for AI applications and the relevance in the clinical practice, I think it fails to outline the limitations to their large-scale deployment.
About the Author
Eric Topol is the Founder and Director of the Scripps Research Translational Institute, Professor, Molecular Medicine, and Executive Vice-President of Scripps Research. As a researcher, he has published over 1,200 peer-reviewed articles, with more than 250,000 citations, elected to the National Academy of Medicine, and is one of the top 10 most cited researchers in medicine. His principal scientific focus has been on the genomic and digital tools to individualize medicine.
In 2016, Topol was awarded a $207 million grant from the National Institutes of Health (NIH) to lead a significant part of the Precision Medicine (All of Us) Initiative, a prospective research program enrolling 1 million diverse participants in the US. This is in addition to his role as principal investigator for a flagship $35 million NIH grant to promote innovation in medicine. Prior to coming to Scripps in 2007, he led the Cleveland Clinic to become the #1 center for heart care and was the founder of a new medical school there. He has been voted as the #1 most Influential physician leader in the United States in a national poll conducted by Modern Healthcare. Besides editing several textbooks, he has published 3 bestseller books on the future of medicine: The Creative Destruction of Medicine and The Patient Will See You Now and the latest Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again in 2019. Topol was commissioned by the UK in 2018-2019 to lead planning for the National Health Service’s integration of AI and new technologies.