4 papers · 1 filter
Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning
Minh-Ha Nguyen, Cathy Shyr
Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a fixed model could adapt its b…
A safety-oriented hypothetico-deductive framework for AI-assisted differential diagnosis
Fan Ma, Mauro Giuffrè, Donald Wright +12
Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often treat diagnosis as a one-shot prediction task, lacking safeguards against…
Teaching agentic AI to generalize expert diagnostic reasoning in rare diseases
Minh-Ha Nguyen, Erica Gray, Bryce A. Schuler +16
Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer. Large language models rank the correct disease first in only 35.4% of benchmark cases a…
An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models
Cathy Shyr, Yan Hu, Rory J. Tinker +8
Phenotyping is fundamental to rare disease diagnosis, but manual curation of structured phenotypes from clinical notes is labor-intensive and difficult to scale. Existing artificia…