Regulating Anatomy-Aware Rewards via Trajectory-Integral Feedback for Volumetric Computed Tomography Analysis
arXiv:2605.20277v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) have rapidly advanced as general-purpose multimodal assistants, yet their deployment in 3D Computed Tomography (CT) analysis remains constrained by a persistent mismatch between optimization objectives and clinical rigor. Current Reinforcement Learning (RL) paradigms still rely on lexical proxy signals that induce ``\textit{Evaluation Hallucinations}'', where models optimize linguistic fluency rather than factual clinical correctness, leading to diagnostically critical errors. To bridge this gap, we introdu
The rapid advancement of medical vision-language models necessitates addressing their practical limitations in critical applications like 3D CT analysis to ensure clinical safety and efficacy.
This research highlights a critical hurdle for medical AI deployment by identifying and proposing a solution to 'Evaluation Hallucinations' in diagnostic systems, directly impacting patient care and regulatory approval.
The proposed 'Regulating Anatomy-Aware Rewards via Trajectory-Integral Feedback' mechanism fundamentally changes how medical VLMs are optimized, moving from linguistic fluency to factual clinical correctness.
- · AI-driven medical diagnostics companies
- · Healthcare providers adopting AI
- · Patients receiving AI-assisted diagnoses
- · AI safety and ethics researchers
- · Developers of un-regulated or purely lexically-optimized medical AI
- · Companies relying on superficial AI performance metrics
Improved reliability and accuracy of AI models in 3D medical image analysis.
Accelerated regulatory approval and clinical adoption of advanced AI diagnostic tools.
Enhanced trust in AI within the medical community, leading to broader integration into patient pathways and reduced diagnostic errors.
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Read at arXiv cs.AI