In-context: July 20, 2025
In-context: July 20, 2025
Here’s a quick wrap of the three papers we found interesting over the last few weeks with some take home points.
1:00 - Clinical knowledge in LLMs does not translate to human interactions
06:45 - From Tool to Teammate: A Randomized Controlled Trial of Clinician-AI Collaborative Workflows for Diagnosis
11:55 - Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study
Some resources and papers we discuss:
Bean, Andrew M. et al. “Clinical knowledge in LLMs does not translate to human interactions.” ArXiv abs/2504.18919 (2025): n. pag.
Everett SS, Bunning BJ, Jain P, Lopez I, Agarwal A, Desai M, Gallo R, Goh E, Kadiyala VB, Kanjee Z, Koshy JM, Olson A, Rodman A, Schulman K, Strong E, Chen JH, Horvitz E. From Tool to Teammate: A Randomized Controlled Trial of Clinician-AI Collaborative Workflows for Diagnosis. medRxiv [Preprint]. 2025 Jun 8:2025.06.07.25329176. doi: 10.1101/2025.06.07.25329176. PMID: 40502554; PMCID: PMC12155023.
https://magazine.sebastianraschka.com/p/understanding-multimodal-llms
Du, Hongru et al. “Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study.” ArXiv abs/2404.06962 (2024): n. pag.