Reinforcement Learning and Vision-Language Models for Intelligent Clinical Report Generation and Medical Image Understanding
Keywords:
reinforcement learning, vision-language models, clinical report generation, medical imaging, fairness, governance, deployment, sociotechnical systemsAbstract
The automated generation of clinical reports from medical images is a grand challenge at the intersection of computer vision, natural language generation, and healthcare delivery. Recent advances in vision-language models (VLMs) have enabled remarkable zero-shot and few-shot image understanding, while reinforcement learning (RL) offers a principled framework for optimizing sequence-level clinical objectives that are non-differentiable and align with diagnostic quality. This paper presents a system-level interdisciplinary analysis of the integration of RL and VLMs for clinical report generation and medical image understanding. We examine architectural paradigms that combine large-scale pretrained multimodal representations with reward-driven fine-tuning tailored to radiological correctness, factual consistency, and clinical utility. The discussion moves beyond algorithm design to consider structural trade-offs involving data governance, infrastructure federation, model robustness, fairness across populations, and regulatory compliance. We analyze how RL-guided relation extraction and graph-structured reasoning can sharpen the alignment between visual findings and textual narratives, while confronting challenges such as hidden stratification, demographic bias, and the risk of fluent but clinically inaccurate generations. The paper further addresses deployment sustainability, energy consumption of foundation models, continuous monitoring in live hospital environments, and the evolving policy landscape defined by FDA action plans and the EU AI Act. By synthesizing technical architectures with sociotechnical governance, we argue that the next generation of intelligent clinical reporting systems must be designed as accountable, transparent, and continuously validated socio-technical infrastructures. The analysis identifies open problems and advocates for interdisciplinary collaboration across machine learning, medicine, ethics, and law to realize safe and equitable clinical AI.
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