#22 Explaining Explainable AI (for healthcare) with Dr Annabelle Painter (RSM digital health section Podcast)

Dev and Doc is joined by guest Annabelle Painter, doctor, CMO, and podcaster for the Royal Society of Medicine Digital Health Podcast. We deep dive into explainability and interpretability with concrete healthcare examples.

Check out Dr. Painter's Podcast here, she has some amazing guests and great insights into AI in healthcare! - https://spotify.link/pzSgxmpD5yb

👋 Hey! If you are enjoying our conversations, reach out, share your thoughts and journey with us. Don't forget to subscribe whilst you're here :)

👨🏻‍⚕️ Doc - Dr. Joshua Au Yeung - https://www.linkedin.com/in/dr-joshua-auyeung/

🤖 Dev - Zeljko Kraljevic - https://twitter.com/zeljkokr

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🎞️ Editor - Dragan Kraljević - https://www.instagram.com/dragan_kraljevic/

🎨 Brand design and art direction - Ana Grigorovici - https://www.behance.net/anagrigorovici027d

Timestamps:

• 00:00 - Start + highlights
• 03:47 - Intro
• 08:16 - Does all AI in healthcare need to be explainable?
• 15:56 - History and explanation of Explainable/Interpretable AI
• 20:43 - Gradient-based saliency and heat maps
• 24:14 - LIME - Local Interpretable Model-agnostic Explanations
• 30:09 - Nonsensical correlations - When explainability goes wrong
• 33:57 - Modern explainability - Anthropic
• 37:15 - Comparing LLMs with the human brain
• 40:02 - Clinician-AI interaction
• 47:11 - Where is this all going? Aligning models to ground truth and teaching them to say "I don't know"

References:

• Fun Examples of when models go wrong - Nonsensical correlations
• Mechanistic interpretability
• Anthropic - Mapping the mind of language models
• Limitations of current AI explainability approaches
• Explainability does not improve automation bias in radiologists