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39 reviews for:
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
Eric Topol
3.74 AVERAGE
39 reviews for:
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
Eric Topol
3.74 AVERAGE
hopeful
informative
inspiring
reflective
sad
medium-paced
I read this for a university project but found myself engrossed on a personal level too - while this book is written through a US lens and I’m from the UK, the general messages are still applicable across the systems. It was saddening to see quite how bad the lack of human connection in medicine has gotten, which resonates with my own experience with doctors over the past few years, however the book inspires hope that if we use technology carefully and thoughtfully things could really change for the better. I’m not too optimistic that AI in medicine will mean more human contact rather than more money for large medical companies, but I think there really is a chance to get this right and only time will tell if we take it.
informative
inspiring
slow-paced
informative
inspiring
slow-paced
Ultimately, this is an optimistic book. "The greatest opportunity offered by AI is not reducing errors or workloads, or even curing cancer: it is the opportunity to restore the precious and time-honored connection and trust—the human touch—between patients and doctors."
There's detail on what machine learning can do right now. That includes diagnosis from radiography, virutal medicine (telemedicine and chatbots), personalisation of healthcare, diet, mental health, and AI as a tool for use by the clinician.
I found it astonishing how much progress has been made. Yet: "We're still in the earliest days of AI in medicine. The field is long on computer algorithmic validation and promises but very short on real-world, clinical proof of effectiveness." Nevertheless, "it is inevitable that narrow AI [specific, targetting algorithms] will take hold".
My takeaway from the book was the hope for "deep empathy": knowing about the patient, their history, and having the time to use that knowledge. "It's our chance, perhaps the ultimate one, to bring back real medicine: Presence. Empathy. Trust. Caring. Being Human."
There's detail on what machine learning can do right now. That includes diagnosis from radiography, virutal medicine (telemedicine and chatbots), personalisation of healthcare, diet, mental health, and AI as a tool for use by the clinician.
I found it astonishing how much progress has been made. Yet: "We're still in the earliest days of AI in medicine. The field is long on computer algorithmic validation and promises but very short on real-world, clinical proof of effectiveness." Nevertheless, "it is inevitable that narrow AI [specific, targetting algorithms] will take hold".
My takeaway from the book was the hope for "deep empathy": knowing about the patient, their history, and having the time to use that knowledge. "It's our chance, perhaps the ultimate one, to bring back real medicine: Presence. Empathy. Trust. Caring. Being Human."
Two stars for me -- this was okay, but that may simply reflect that this seems to be intended as a high-level overview of AI and similar technologies and their current and future effects on the practice of medicine and healthcare. As someone who works in health IT and follows issues related to AI, machine learning, the economics of heath care, and so on, there was very little in this book that was new to me. If you're looking for a high-level overview of AI and healthcare, this is a good place to start.
I do like Topol's emphasis on having AI, machine learning, and so on, be used to allow doctors to do a better job connecting with patients. It's become obvious in recent years that so-called "social determinants of health" are in many ways much better predictors of health and illness -- in other words, knowing where someone lives and what kind of family relationships they have is, in many cases, more useful than a large battery of fancy lab tests. Perhaps in a similar way, AI can allow us to reconceptualize the doctor-patient relationship as more of a trusted, close advisor/friend.
I do wonder about how much AI and other technology can really enable Topol's "deep empathy" . I see two problems: Baumol's cost disease and regular capitalistic incentives.
First, there's Baumol's cost disease. It seems that doctors and patients would prefer, say, 30 minute visits. But the cost disease argument is that if you have something defined by a fixed amount of time, its cost must rise. Yes, algorithms and machine learning diagnostic tools can improve the care, but if providers and patients want that 30 minutes, that cost must necessarily rise, and you can't get the sort of order-of-magnitude productivity gains that you need to really change costs.
The second thing relates to the larger economic system driven by profit or revenue. Say you've got your 30-minute visits. Doctors and patients like it. Say doctors have 8 hours, or 16 patients, of time a day. But the healthcare system leadership thinks "if we make those visits 25 minutes, the doctors can see 19 patients a day! That's over 18% more patients every day!" But the same logic keeps applying, and before long we're back with the ultra-short rushed visits we have now.
This isn't necessarily about moustache-twirling predatory capitalists; even government-driven programs like Medicare or the NHS can apply this kind of pressure. In addition to longer visits, though, patients also want better access, both for scheduling convenience and medical necessity -- no one wants to wait to see the doctor if you are sick and want advice, but not so sick that emergenccy or urgent care is right.
b
I do like Topol's emphasis on having AI, machine learning, and so on, be used to allow doctors to do a better job connecting with patients. It's become obvious in recent years that so-called "social determinants of health" are in many ways much better predictors of health and illness -- in other words, knowing where someone lives and what kind of family relationships they have is, in many cases, more useful than a large battery of fancy lab tests. Perhaps in a similar way, AI can allow us to reconceptualize the doctor-patient relationship as more of a trusted, close advisor/friend.
I do wonder about how much AI and other technology can really enable Topol's "deep empathy" . I see two problems: Baumol's cost disease and regular capitalistic incentives.
First, there's Baumol's cost disease. It seems that doctors and patients would prefer, say, 30 minute visits. But the cost disease argument is that if you have something defined by a fixed amount of time, its cost must rise. Yes, algorithms and machine learning diagnostic tools can improve the care, but if providers and patients want that 30 minutes, that cost must necessarily rise, and you can't get the sort of order-of-magnitude productivity gains that you need to really change costs.
The second thing relates to the larger economic system driven by profit or revenue. Say you've got your 30-minute visits. Doctors and patients like it. Say doctors have 8 hours, or 16 patients, of time a day. But the healthcare system leadership thinks "if we make those visits 25 minutes, the doctors can see 19 patients a day! That's over 18% more patients every day!" But the same logic keeps applying, and before long we're back with the ultra-short rushed visits we have now.
This isn't necessarily about moustache-twirling predatory capitalists; even government-driven programs like Medicare or the NHS can apply this kind of pressure. In addition to longer visits, though, patients also want better access, both for scheduling convenience and medical necessity -- no one wants to wait to see the doctor if you are sick and want advice, but not so sick that emergenccy or urgent care is right.
b
Written by an established practitioner and expert, Topol argues that AI (mostly deep learning) could be applied to help medical practitioners to automate the pattern recognition part of their job and to let them focus solely on their main task: helping patients.
informative
reflective
slow-paced
I revere Topol on twitter as he never fails to inspire, but perhaps it is with such high expectations that the book turned into a disappointment. In short, it seemed more like a compilation of researches and advances in Deep Medicine, without as much context from the author's point of view.
This is a wonderful and timely book. The era of artificial intelligence and machine learning and their roles and contributions to the practice and advancement of medicine is still at its very early stages. This book offers an extensive review of the most recent efforts in that regard. Eric Topol is a great writer, with a clear clinician’s insight into the potential effects of AI and machine learning in the medical practice. He discusses critical aspects including medical education, the patient-physician relationship, the practice of medicine in specific fields such as oncology and cardiology, as well as those specialties that have a big role of pattern recognition such as pathology, radiology, ophthalmology, and dermatology.