ClinDEF: A Dynamic Framework for Evaluating LLMs in Clinical Reasoning
Published:Dec 29, 2025 12:58
•1 min read
•ArXiv
Analysis
This paper introduces ClinDEF, a novel framework for evaluating Large Language Models (LLMs) in clinical reasoning. It addresses the limitations of existing static benchmarks by simulating dynamic doctor-patient interactions. The framework's strength lies in its ability to generate patient cases dynamically, facilitate multi-turn dialogues, and provide a multi-faceted evaluation including diagnostic accuracy, efficiency, and quality. This is significant because it offers a more realistic and nuanced assessment of LLMs' clinical reasoning capabilities, potentially leading to more reliable and clinically relevant AI applications in healthcare.
Key Takeaways
- •ClinDEF is a dynamic framework for evaluating LLMs in clinical reasoning.
- •It simulates doctor-patient dialogues for a more realistic assessment.
- •The framework uses a disease knowledge graph to generate patient cases.
- •Evaluation includes diagnostic accuracy, efficiency, and quality.
- •ClinDEF reveals clinical reasoning gaps in state-of-the-art LLMs.
Reference
“ClinDEF effectively exposes critical clinical reasoning gaps in state-of-the-art LLMs, offering a more nuanced and clinically meaningful evaluation paradigm.”