AI clinical

Podcast: Clinicians’ ‘Number-One Wish’ for Artificial Intelligence

…we installed cheap depth sensors that can collect human behavior data on patients and clinicians without infringing on their privacy, because these are not photo grabs of people’s faces and identities. With that information, we can observe longitudinally, 24/7, whether proper care is being given to our patients and provide feedback in the health delivery system.

Topol, E. & Li, F. (2020). Clinicians’ ‘Number-One Wish’ for Artificial Intelligence. Medicine and the Machine podcast. Medscape.

I didn’t hear what the number one wish was (I was driving to work and may have been distracted for a moment) but the conversation is generally worth listening to. Topol and Li both have good insight into the application of AI in clinical contexts and the conversation touches on some of the technical aspects of AI (e.g. bias, training machine learning algorithms, labeled datasets, etc.) while staying accessible for listeners who are unfamiliar with the details.

One of the standout bits for me was the discussion around how the use of depth sensors in an ICU can generate data that an AI can use to map the behaviour of staff within the unit, to the extent that it can tell whether or not basic levels of care are being met. You might have concerns about issues of privacy and the surveillance of staff but if one of my family members were in an ICU, I know that I’d want to know if everyone is washing their hands appropriately.

The link above includes a transcript of the conversation.

AI clinical

Article: Resistance to Medical Artificial Intelligence

Across a variety of medical decisions ranging from prevention to diagnosis to treatment, we document a robust reluctance to use medical care delivered by AI providers rather than comparable human providers.

Whereas much is known about medical AI’s accuracy, cost-efficiency, and scalability, little is known about patients’ receptivity to medical AI. Yet patients are the ultimate consumers of medical AI, and will determine its adoption and implementation both directly and indirectly.

Chiara Longoni, Andrea Bonezzi, Carey K Morewedge, Resistance to Medical Artificial Intelligence, Journal of Consumer Research, Volume 46, Issue 4, December 2019, Pages 629–650.

This is a long paper analysing 9 studies that look at patient preferences when comparing health services that are either automated or provided by human beings. I think it’s an important article that covers a wide range of factors that need to be considered in the context of clinical AI. We’re spending a lot of money on research and development into AI-based interventions but we know almost nothing about how patients will engage with it.

Note: This is a nice idea for a study looking at patient preferences in rehabilitation contexts where we’re likely to see the introduction of robots, for example. I’d be interested to know if there are any differences across geography, culture, etc. Let me know if you’re keen to collaborate.