The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Explainable AI for Biology and Medicine with Su-In Lee - #642
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Aug 14, 2023 Su-In Lee, a professor at the University of Washington's Paul G. Allen School of Computer Science, discusses her research on explainable AI in biology and medicine. She emphasizes the importance of interdisciplinary collaboration for improving cancer and Alzheimer's treatments. The conversation delves into the robustness of explainable AI techniques, the challenges of handling biomedical data, and the role of machine learning in drug combination therapies. Su-In also highlights innovative methods for personalized patient care and predictive insights in oncology.
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Beyond Feature Attribution
- Explainable AI (XAI) in biomedicine needs to move beyond basic feature attribution.
- It should reveal how features collaborate, offering system-level insights, not just individual feature importance.
Dermatology Image Example
- Su-In Lee uses a dermatology image example to explain this.
- Knowing which pixels contribute to a melanoma diagnosis isn't enough; understanding how they contribute is key.
XAI for Cancer Therapy
- Su-In Lee's research aims to improve XAI for biology and healthcare.
- Her team's recent work focuses on using XAI to improve cancer therapy design.