SiMing-Bench shows current MLLMs have weak agreement with physicians on procedural correctness in clinical videos, with intermediate step judgments remaining poor even when overall scores look acceptable.
arXiv preprint arXiv:2602.23363 (2026)
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Scoping review of 134 studies on LLM-as-a-Judge in healthcare finds concentration in clinical decision support and NLP, frequent use of OpenAI models with prompt engineering, and moderate-to-strong human alignment where validated.
CogAlign uses hierarchical supervised fine-tuning on clinical cognition data plus counterfactual RL to align MLLMs with expert diagnostic pathways and enforce causal lesion grounding for GI endoscopy diagnosis.
MedSynapse-V proposes a latent memory evolution framework with meta-query prior retrieval, causal counterfactual refinement via RL, and intrinsic memory transition to improve diagnostic accuracy over chain-of-thought baselines in medical VLMs.
citing papers explorer
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SiMing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos
SiMing-Bench shows current MLLMs have weak agreement with physicians on procedural correctness in clinical videos, with intermediate step judgments remaining poor even when overall scores look acceptable.
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LLM-as-a-Judge in Healthcare: A Scoping Analysis of Applications, Methods, and Human Alignment
Scoping review of 134 studies on LLM-as-a-Judge in healthcare finds concentration in clinical decision support and NLP, frequent use of OpenAI models with prompt engineering, and moderate-to-strong human alignment where validated.
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Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs
CogAlign uses hierarchical supervised fine-tuning on clinical cognition data plus counterfactual RL to align MLLMs with expert diagnostic pathways and enforce causal lesion grounding for GI endoscopy diagnosis.
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MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V proposes a latent memory evolution framework with meta-query prior retrieval, causal counterfactual refinement via RL, and intrinsic memory transition to improve diagnostic accuracy over chain-of-thought baselines in medical VLMs.