Generative AI in medicine: Improving patient & provider experiences
summary
Generative AI work focuses on concrete wins: using AWS SageMaker, Bedrock and Databricks plus RAG to build knowledge management systems that compress thousands of research documents, auto‑author clinical trial patient narratives from 4 hours to under a minute, and speed FDA submission drafting; teams pair strong MLops and LLM ops with FinOps to control GPU costs, prefer extending foundation models over building from scratch, and pilot plain‑language consent generation to improve patient comprehension.
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chapters (11)
- 0:00Introduction and perspectives
- 3:55Strategic vision for gen AI
- 7:13Data and operational goals
- 10:17Greatest impact areas
- 12:52Measuring value
- 16:29Lessons learned
- 20:24Risks and quality assurance
- 23:43Managing AI costs
- 26:42Practical use cases
- 33:09Governance and talent
- 39:25Buy versus build strategies
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