arXiv:2606.26874v1 Announce Type: new Abstract: Transcatheter Aortic Valve Replacement (TAVR) planning requires meticulous multimodal reasoning. However, adapting Multimodal Large Language Models (MLLMs) to this high-stakes domain is severely impeded by diagnostic hallucinations, where generated text lacks anatomical grounding. To address this, TAVR-VLM is introduced: a novel framework featuring Risk-Conditioned Causal Grounding Attention (R-CGA) that instantiates a model-internal ``Risk $\rightarrow$ Region $\rightarrow$ Word'' structural grounding pathway. R-CGA compresses multimodal inputs
The increased adoption of MLLMs in high-stakes domains necessitates robust solutions to address critical issues like diagnostic hallucinations, driving research into grounding mechanisms.
This development represents a significant step towards enabling trustable AI applications in sensitive sectors like medicine, expanding the practical utility and safety of multimodal AI.
The introduction of TAVR-VLM shifts the focus from purely generating text to ensuring anatomical and causal grounding in medical AI reports, making MLLMs more reliable for clinical use.
- · Healthcare AI developers
- · Medical professionals
- · Patients needing TAVR planning
- · Multimodal LLM researchers
- · Developers of ungrounded medical AI models
- · General-purpose MLLMs lacking specialization
Improved accuracy and reliability of AI-generated medical reports due to reduced hallucinations.
Accelerated adoption of AI in diagnostic processes, reducing human workload and potential errors.
The development of similar grounding frameworks across other high-stakes AI applications beyond healthcare, creating a new standard for AI robustness.
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Read at arXiv cs.AI