As a result of the explosive growth of artificial intelligence (AI) in recent years, it is predicted that up to 47 percent of jobs may be automated away in the future [1]. Such disruption is often focused on blue-collar professions. However, recent advances in deep learning have raised the possibility that many white-collar professions may be likewise disrupted. In no profession is this more the case than in medicine, the de facto proving ground for many modern AI algorithms. But if such a disruption were to occur, and many tasks that doctors presently undertake were to be automated away, what would this mean for the medical profession at large? What would it mean for the doctors trained to perform such tasks? And, perhaps most importantly, what would it mean for patients?
These are just some of the questions touched upon by Eric Topol in his latest book. Topol is one of the world’s leading cardiologists and has authored numerous books on the role of technology in shaping the future of medicine. In this book, he sets his sights on the recent advances within the field of AI and, in particular, deep learning. He asks whether these advances will impact different disciplines within the field of medicine and, if so, what this impact would mean for doctors and patients alike.
Drawing on numerous personal experiences, as well as a range of voices from the field of deep learning and medicine, Topol gently introduces readers to what he terms “deep medicine”: leveraging the power of deep learning to improve patient care, reduce treatment costs, and help clinicians make more effective and patient-centric use of their time, that is, helping doctors return to the task of caring for their patients with skill and empathy.
At the heart of this book is a positive message--change is coming, and the “traditional roles” of many clinical specialisms will most likely be automated away, but this is ultimately a good thing. It is a good thing for clinicians and it is a good thing for patients. Topol envisions a future where the machine will automate many of the mundane and routine tasks that clinicians are forced to endure today, for example, performing standard screening tasks or reviewing and collating patient notes. This automation will give doctors more time to focus on patient needs.
Even as a nonclinician, there is something deeply appealing about the empathetic and patient-centric future that Topol paints. Anyone who has visited their doctor in recent times will know the pressure they are under and the intense time constraints they face. This has a negative impact not only on the patient, but also on the mental well-being of the doctor. Some of the statistics cited in this regard make for bleak reading. But Topol believes that AI presents a possible way out of this time-constrained “narrow” medicine. AI has the potential not to replace doctors, but to assist them, to free up their time to focus more on the patient and less on the clock.
Topol’s writing style is engaging and accessible. There are just the right number of personal anecdotes and powerful statistics, meaning that the reader’s attention is kept throughout and, given the subject matter, the book is relevant to a wide range of readers, not just those in machine learning or medicine fields.
There is no doubt that advances in AI will change the way in which healthcare is delivered. However, this change need not be detrimental, and the future is bright. Topol presents a tantalizing glimpse of this future, whether or not it is ever reached. Only time will tell.
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