6 papers
MMedExpert-R1: Strengthening Multimodal Medical Reasoning via Domain-Specific Adaptation and Clinical Guideline Reinforcement
Meidan Ding, Jipeng Zhang, Wenxuan Wang +4
Medical Vision-Language Models (MedVLMs) excel at perception tasks but struggle with complex clinical reasoning required in real-world scenarios. While reinforcement learning (RL)…
MedEinst: Benchmarking the Einstellung Effect in Medical LLMs through Counterfactual Differential Diagnosis
Wenting Chen, Zhongrui Zhu, Guolin Huang +1
Despite achieving high accuracy on medical benchmarks, LLMs exhibit the Einstellung Effect in clinical diagnosis--relying on statistical shortcuts rather than patient-specific evid…
Med-RewardBench: Benchmarking Reward Models and Judges for Medical Multimodal Large Language Models
Meidan Ding, Jipeng Zhang, Wenxuan Wang +6
Multimodal large language models (MLLMs) hold significant potential in medical applications, including disease diagnosis and clinical decision-making. However, these tasks require…
Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models
Wenting Chen, Guo Yu, Yiu-Fai Cheung +5
Large language models (LLMs) show significant potential in healthcare, prompting numerous benchmarks to evaluate their capabilities. However, concerns persist regarding the reliabi…
A Survey of LLM-based Agents in Medicine: How far are we from Baymax?
Wenxuan Wang, Zizhan Ma, Zheng Wang +5
Large Language Models (LLMs) are transforming healthcare through the development of LLM-based agents that can understand, reason about, and assist with medical tasks. This survey p…
Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence
Jie Liu, Wenxuan Wang, Zizhan Ma +7
Clinical decision making (CDM) is a complex, dynamic process crucial to healthcare delivery, yet it remains a significant challenge for artificial intelligence systems. While Large…