Explainable AI in Medicine
Su discusses the limitations of current explainable AI methods in medicine, emphasizing the need for system-level insights rather than just feature-level explanations. She highlights how new approaches can facilitate collaboration between AI and clinical experts, particularly in cancer therapy design. The conversation also touches on foundational AI methods that unify existing literature and improve computational efficiency, paving the way for more effective applications in biology and health care.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Explainable AI for Biology and Medicine with Su-In Lee - 642
Related Questions
How can we make AI more explainable in the context of the episode Explainable AI for Biology and Medicine with Su-In Lee - 642 and the clip Explainable AI in Medicine?
How can AI be made explainable in the context of the episode Explainable AI for Biology and Medicine with Su-In Lee - 642 and the clip Explainable AI in Medicine?
What is the challenge around explainability in AI?