Works in Progress Podcast Will AI solve medicine?
19 snips
Oct 29, 2025 The discussion explores the transformative potential of AI in drug development, tracing its journey from discovery to delivery. Insights reveal AI's strengths in enhancing discovery but highlight the limitations posed by biological complexities and data availability. The hosts debate the need for empirical methods alongside AI, while emphasizing the challenges of recruiting participants for clinical trials. They also analyze economic incentives that skew R&D priorities and caution against misinformation in public health. Ultimately, AI aids innovation but cannot tackle all barriers alone.
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Follow Rigorous Clinical Trial Stages
- Design clinical trials progressively: IND, phase I (safety), phase II (safety and signals), then larger phase III confirmatory trials.
- Continue post-approval surveillance because rare or later safety issues often appear after market entry.
Thicket Rat Malaria Discovery
- Discovering useful animal models can be painstaking and serendipitous, such as finding a thicket rat infected with rodent malaria.
- Researchers tested hundreds of animals and waited for outbreaks before locating an applicable model.
Trial Design Strongly Dictates Speed
- Trials stall due to recruitment, low incidence, and long follow-up; challenge trials and platform trials can shorten timelines but carry ethical and logistical trade-offs.
- COVID vaccine trials succeeded due to high incidence, parallel phases, and rolling regulatory review.
