

#309 ‒ AI in medicine: its potential to revolutionize disease prediction, diagnosis, and outcomes, causes for concern in medicine and beyond, and more | Isaac Kohane, M.D., Ph.D.
333 snips Jul 15, 2024
Isaac Kohane, a physician-scientist and chair of Biomedical Informatics at Harvard Medical School, shares his insights on the transformative role of AI in medicine. He discusses advancements in disease prediction and early diagnosis, including exciting developments in robotic surgery. Kohane also addresses ethical concerns and the need for responsible integration of AI in healthcare. The conversation highlights the balance between leveraging AI's potential and mitigating risks such as bias and job displacement, making for a thought-provoking exploration of the future of medicine.
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Unconventional Path to AI in Medicine
- Isaac Kohane's non-traditional path involved studying biology and computer science, attending medical school, and pursuing a PhD in computer science during AI's second wave.
- His advisor recommended clinical training for broader acceptance of AI in medicine, leading him to complete medical training before returning to computing research.
The Evolving Definition of AI
- Alan Turing's test proposed that if a computer could convincingly impersonate a human in conversation, it could be considered intelligent. However, the definition of intelligent behavior in AI is constantly evolving.
- What was once considered AI, like beating chess masters, is now seen as sophisticated search, highlighting the shifting goalposts of intelligence.
Limitations of Rule-Based AI
- Second-wave AI used rule-based systems programmed in human language, but these systems proved brittle and difficult to maintain. They relied on expert knowledge, which was costly and hard to keep up-to-date, and struggled with complex interactions.