Episode 17: Garbage Data, Toxic Exposure, and the AI Gap in Environmental Health

In this episode of From Models to Medicine, we sit down with Sarah Daniels, an environmental health researcher, who makes the argument that the chemicals making us sick might also be our best clues for building better drugs. She traces that idea from a 1976 discovery about dioxins all the way to an FDA-approved psoriasis treatment in 2023 — and asks what AI could accelerate if the field finally got its data act together.Sarah brings up where AI in environmental health is genuinely stuck; siloed industry data, models built only for acute toxicity, and a chronic low-dose problem that no one has cracked. She also shares how she used APIs to build her own real-time funding tracker for microplastics research in under a month, and why the gap between what AI promises and what environmental health can actually feed it is one of the most under appreciated bottlenecks in the field right now.Links mentioned in this episode: Historic ARPA-H program funding announcement on Microplastics: https://arpa-h.gov/explore-funding/programs/stompSarah's two cents on the ARPA-H Microplastics program:  https://engineeredresilience.substack.com/p/a-microplastics-prelude-for-engineeredAggregated trends in microplastics from the non-peer-reviewed literature (Trendspotter App):https://www.engineeredresilience.org/microplasticsStay up to date on latest releases from Sarah's work on Engineered Resilience by signing up for substack: https://engineeredresilience.substack.com/Report on unexpected health effects observed after LA fires showing how questions remain post-exposure to pollutants:https://www.latimes.com/environment/story/2025-12-17/la-fires-heart-attacks-strange-blood-test-results-spikedCan a Biologist Fix a Radio? - great piece on the necessity to understand biology at the systems-level: https://www.cell.com/cancer-cell/fulltext/S1535-6108(02)00133-2