AI and Health: Hype or Reality? Evaluate the Evidence With This Cheat Sheet
One of the trends that consistently receives significant attention in the innovation arena is artificial intelligence (see the latest edition of my digihealth impact trac quarterly report for more details). Clearly investors are fans of companies developing solutions in this sector given the large amount of funding startups are receiving.
Despite its promise, clinicians, providers and others are skeptical. They wonder whether and how convincing and widespread evidence will emerge that AI-powered solutions have a positive impact on health outcomes.
This was a topic I tackled during my first digihealth impact trac live briefing held earlier today. (These are regular, intimate, online sessions with innovators with special interest in key digital health topics such as digital therapeutics, artificial intelligence and big data.)
During the briefing, I presented a framework innovators can use to quickly classify and evaluate evidence developed by health AI firms (see image below).
While digihealth impact trac live briefings are open to a limited audience, I’m sharing excerpts from these briefings (and other lectures) via a new podcast series, digital health maven dot edu.
In the first installment of the podcast (15 minutes) I describe and provide examples of each of these levels of evidence.
To listen to episode 1 of digital health maven dot edu, please click here.
You can also subscribe to this podcast on Stitcher and iTunes.
Learn More About Me
I’m an analyst, futurist, researcher and inventor. Since 2005, I’ve been helping people around the world develop better insights and strategy that helps them understand how to navigate the complex and expanding global digital health landscape. Learn about my work, upcoming speaking engagements and more via the links below.
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