Explainable AI for Biology and Medicine with Su-In Lee - #642
Analysis
This article summarizes a podcast episode featuring Su-In Lee, a professor at the University of Washington, discussing explainable AI (XAI) in computational biology and clinical medicine. The conversation highlights the importance of XAI for feature collaboration, the robustness of different explainability methods, and the need for interdisciplinary collaboration. The episode covers Lee's work on drug combination therapy, challenges in handling biomedical data, and the application of XAI to cancer and Alzheimer's disease treatment. The focus is on making meaningful contributions to healthcare through improved cause identification and treatment strategies.
Key Takeaways
- •Explainable AI is crucial for feature collaboration in computational biology and clinical medicine.
- •Robustness of different explainability approaches is a key consideration.
- •Interdisciplinary collaboration between computer science, biology, and medicine is essential for progress.
“Su-In Lee discussed the importance of explainable AI contributing to feature collaboration, the robustness of different explainability approaches, and the need for interdisciplinary collaboration between the computer science, biology, and medical fields.”