E-MRL: Cross-view Aligned Evidence-driven Multimodal Reinforcement Learning for Reliable 3D Tumor Analysis
arXiv:2606.23888v1 Announce Type: cross Abstract: While Vision-Language Models (VLMs) show great promise in volumetric medical report generation, they frequently suffer from visual hallucinations and a lack of grounding in 3D CT data. Current Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) strategies typically optimize text fidelity alone, essentially rewarding correct diagnoses derived from language priors rather than genuine visual perception. To address this, we propose cross-view aligned Evidence-driven Multimodal Reinforcement Learning (Evidence-MRL, noted as E-MRL), a reliab
The increasing sophistication and widespread application of Vision-Language Models in medical imaging are highlighting their inherent limitations, particularly concerning visual hallucinations and accuracy in critical diagnostic fields, necessitating robust solutions like E-MRL.
This research addresses fundamental problems in AI's reliability for high-stakes applications such as medical diagnosis, demonstrating a critical step towards safe and trustworthy AI systems in healthcare.
The focus for medical AI shifts from mere text fidelity to genuinely grounded visual perception, integrating reinforcement learning with evidence-driven multimodal approaches to enhance diagnostic accuracy and reduce hallucinations.
- · Medical AI developers
- · Healthcare providers
- · Patients needing accurate diagnoses
- · Reinforcement Learning researchers
- · AI models prone to hallucination
- · Purely language-prioritized medical AI approaches
- · Those relying on ungrounded VLM outputs
Improved accuracy and trustworthiness of AI-powered medical diagnostics, particularly in 3D tumor analysis.
Accelerated adoption of AI in clinical settings due to increased reliability and regulatory acceptance.
A foundational shift in AI development methodologies for critical applications, emphasizing evidence-driven learning over pure data correlation.
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Read at arXiv cs.AI