A Sustainable Future for Learning AI in Healthcare
summary
A sustainable path rests on concrete fixes: a systematic review finds only 10 RCTs out of 8,000 supervised deep‑learning studies, radiology accounts for 70% of FDA AI approvals with 155 pre‑market notifications in Oct 2023, and clinical trials spend 80% of budget on data curation within a ~$50B market. The proposal builds patient‑centric adaptive clinical trials, data‑recycling ecosystems (quintuple‑helix collaboration), LLM‑enabled productivity to validate proximal outcomes, and targeted deployments like AI double reading of mammography and radiology report self‑supervision.
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chapters (10)
- 0:00Welcome
- 3:58Open learning AI in healthcare
- 8:13AI risks in healthcare
- 12:27AI adoption in healthcare
- 15:58Sustainable AI adoption
- 24:04Generative AI productivity
- 30:12Clinical trials for AI innovation
- 35:08AI knowledge ecosystems
- 43:15Key takeaways
- 47:53AI cooperation continuum
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