By Dr. Ron Razmi

A few weeks ago, I attended the World Economic Forum in Davos and the discussion of AI as the next great technology dominated every corner of this important meeting. From the main event to almost every side event, it seemed that Artificial Intelligence had to be part of every agenda. The breakthroughs that made modern AI possible happened over a decade ago and now we’re starting to actually learn how to use it in every industry. Healthcare is no exception. I have a hard time reading a newsletter or any medical journals these days without running into an article about the applications of AI in healthcare. As a former Cardiologist, who has spent the last 15 years on the business side of healthcare, a good part of those years building a health AI company and writing a book about this topic, I couldn’t be more excited to see all of this. Yet, I spend most of my time writing and speaking about how there are major barriers that will slow down the diffusion of this technology into healthcare.

In my new book, AI Doctor: The Rise of Artificial Intelligence in Healthcare, I devote an entire chapter to these barriers to AI adoption in healthcare and another one to discussing the business models for healthcare executives considering investments in AI solutions. Both of these topics merit some discussion in this article and until they’re addressed, we will not start seeing much of the promise for this technology in healthcare. As I write in the book, I’m very optimistic about what AI will eventually achieve to improve everyone’s health but see a tough road ahead to achieve many of the high-value use cases. Why is that?

If there’s anything that is not a luxury in AI, it’s data. And, when it comes to health, AI needs complete and accurate data. Amazon and Netflix can figure out something about your shopping and tv viewing habits with the limited time you spend on their sites and improve your overall experience. If they get something wrong or if their algorithms mess up, c'est la vie. There will be no real harm that will come your way from those issues. In healthcare, one piece of missing or old data, like your creatinine level, can change the entire picture of your health. An algorithm that makes recommendations without the missing data can do real harm to you. If those using AI for doing research about health do not have access to high-quality data in genomics, proteomics, phenotype data, labs, etc., their research efforts will be significantly hampered, and AI would provide limited benefit.

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