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Eric Topol is one of the world’s leading advocates for AI in medicine. He believes the technology can not only liberate physicians from the growing burden of clerical work, but also synthesize layers of patient data — behavioral, genomic, microbiomic, and so on — into truly personalized healthcare. A cardiologist and geneticist at Scripps Research Institute in Southern California, he is the author of Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Below he shares his insights into the fusion of AI and medicine and advice for machine learning engineers who want to get involved.

The Batch: Let’s start with the topic on everyone’s mind: Where do you see AI’s greatest potential in addressing the Covid-19 pandemic?

Topol: One thing that’s been overlooked is the ability to develop and validate algorithms for at-home monitoring. We don’t want everyone who has Covid-19 symptoms to go to the hospital. On the other hand, some people who catch Covid-19 have sudden demise, and it’s hard to predict. If we could tell who’s safe to monitor at home, that would be great help in managing this epidemic around the world.

The Batch: You’re concerned with the depersonalization of doctor-patient relationships. How can AI help?

Topol: Four words: the gift of time. Clinicians spend too much of their time being data clerks. There shouldn’t be any need for a screen and a keyboard to see a patient. Entering notes into the medical record should be done by AI.

The Batch: Researchers have had experimental success interpreting medical images. Yet these innovations haven’t had much impact on clinical practice. What’s the holdup?

Topol: The medical community feels threatened that the machines will encroach on their lives. Also, some companies working on things like this have proprietary algorithms and don’t publish their data, so there’s a lack of transparency. They get their FDA clearance based on retrospective studies and use the same data over and over, because there aren’t many large, annotated medical datasets. We need prospective studies based on real-world patients in multiple real-world clinical settings. And we need more randomized trials — there have been only six or seven of those.

The Batch: If you could collect any data you wanted for everyone in the world, what would it be, and for what AI task?

Topol: That’s easy: We need a planetary health system. We’d have multilevel data for every person, and each person would teach the rest of their species about preventing and managing illnesses using nearest neighbor analysis and other tools of AI. It’s possible now, but it requires an international commitment. I wrote about this with my colleague Kai-Fu Lee in an article called “It Takes a Planet.”

The Batch: How can we build a planetary health system that protects data privacy and security?

Topol: The tools are in front of us now. We can use federated and homomorphic computing. No country has to hand their data over. The algorithms can be used at the locale.

The Batch: Much of the AI community is deeply concerned about making sure the technology is used ethically. What should AI practitioners keep in mind in that regard?

Topol: Anything that exacerbates the very significant health inequalities that exist today is not acceptable. Human bias that finds its way into algorithms is a significant ethical concern that needs extensive review and scrutiny. And that’s not all. Algorithms in medicine need to be under constant surveillance because if an algorithm is hacked, it could hurt a lot of people.

The Batch: What advice would you give machine learning engineers who want to make a positive impact in medicine?

Topol: We’re still in the early phase. We need more interdisciplinary or transdisciplinary efforts between clinicians and AI practitioners. We need more large, annotated datasets, or to use self-supervised learning that preempts the need for them. We need to go to a higher validation plane, however we get there. Then we’ll be able to take advantage of this extraordinary opportunity to transform medicine and return the human essence that has been largely lost.


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