3 papers
cs.AI2026
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…
cs.AI2026
Teaching agentic AI to learn expert reasoning for rare disease diagnosis
Minh-Ha Nguyen, Erica Gray, Bryce A. Schuler +17
Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer; off-the-shelf large language models (LLMs) rank the correct disease first in only 35.4%…
cs.AI2026
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…