arXiv:2606.12590v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved strong performance across medical imaging tasks, yet they remain prone to factual inconsistencies, poor visual grounding, and misalignment with clinically meaningful feedback. Existing post-training alignment approaches, including Direct Preference Optimization (DPO) and its variants, face three critical limitations in the medical domain: (1) sequence-level reward signals treat clinically critical tokens identically to generic filler text; (2) reliance on static supervised fine-tuning reference
The rapid advancement of Large Vision-Language Models (LVLMs) is pushing their application into critical domains like medicine, where existing alignment techniques are proving insufficient for safety and accuracy.
Improving fine-grained preference optimization in medical LVLMs is crucial for their reliable deployment, directly impacting diagnostic accuracy, treatment recommendations, and patient safety.
The focus is shifting from generic LVLM performance to domain-specific, nuanced alignment, addressing factual inconsistencies and poor grounding in high-stakes applications.
- · Medical AI developers
- · Healthcare providers
- · Patients receiving AI-assisted care
- · Ethical AI frameworks
- · Developers of general-purpose LVLMs lacking domain adaptation expertise
- · Healthcare systems relying on unvalidated AI
- · Traditional diagnostic methods
More accurate and trustworthy AI tools become available for medical diagnostics and research.
Accelerated adoption of AI in clinical settings due to increased reliability and reduced risk.
New regulatory frameworks specifically for medical AI's performance and ethical alignment emerge globally.
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Read at arXiv cs.AI