Domain specialization does not consistently improve clinical LLM robustness to meaning-preserving prompt variations, as shown by new sensitivity metrics on DiagnosisQA and MedQA.
A comprehensive survey on the trustworthiness of large language models in healthcare.arXiv preprint arXiv:2502.15871, 4
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PolicyAlign aligns LLMs to natural-language safety policies by synthesizing violating instructions and performing on-policy self-distillation with policy-sensitive filtering, improving safety without high-quality supervision data.
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
SURGENT is a multi-agent surgical assistance system with novel memory management that outperforms baseline LLMs on case analysis, plan simulation, safety monitoring, risk assessment, and rehabilitation guidance.
Context alignment in medical VLMs raises AUC from 0.918 to 0.925, cuts hallucinated keywords from 1.14 to 0.25, shortens explanations to 15.3 words, and maintains calibrated uncertainty without raising model confidence.
citing papers explorer
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Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs
Domain specialization does not consistently improve clinical LLM robustness to meaning-preserving prompt variations, as shown by new sensitivity metrics on DiagnosisQA and MedQA.
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PolicyAlign: Direct Policy-Based Safety Alignment for Large Language Models
PolicyAlign aligns LLMs to natural-language safety policies by synthesizing violating instructions and performing on-policy self-distillation with policy-sensitive filtering, improving safety without high-quality supervision data.
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UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
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SURGENT: A Surgical Multi-Agent Assistance System Across the Perioperative Workflow
SURGENT is a multi-agent surgical assistance system with novel memory management that outperforms baseline LLMs on case analysis, plan simulation, safety monitoring, risk assessment, and rehabilitation guidance.
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Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Models
Context alignment in medical VLMs raises AUC from 0.918 to 0.925, cuts hallucinated keywords from 1.14 to 0.25, shortens explanations to 15.3 words, and maintains calibrated uncertainty without raising model confidence.