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Early evidence suggests that medical world models could revolutionize patient care by enabling dynamic, intervention-responsive AI systems, but critical hurdles remain.
Current evaluation practices for agentic AI in medicine are misaligned with clinical needs, risking the reliability of these systems in real-world applications.
DDVT achieves superior answer grounding in visual question answering by seamlessly integrating visual and textual features, outperforming existing methods on key benchmarks.
