Tag
This paper presents an evaluation of multi-turn multimodal diagnostic reasoning using challenging real-world clinical cases, aiming to assess AI models' ability to handle complex medical scenarios.
This paper proposes a reinforcement learning framework for evidence-seeking diagnostic reasoning using LLMs. The RL-trained 7B model outperforms larger models in multilingual clinical consultation tasks, showing that specialized RL can distill high-level clinical reasoning.
This study evaluates how prompting language (English vs. French) affects diagnostic reasoning and accuracy across five LLMs using 180 clinical vignettes, finding that most models perform significantly better in English, with o3 being the only exception.
UMBC researchers show LLMs judge scientific claim feasibility better when given outcome data than experiment descriptions, and that incomplete experimental context can hurt accuracy.