Medical VLMs frequently select negated options that contradict visible chest X-ray findings, achieving only ~30% accuracy on direct presence probes, but a post-hoc consistency verifier raises accuracy above 95%.
Acosta, Josh Miller, Ouwen Huang, and Pranav Rajpurkar
3 Pith papers cite this work. Polarity classification is still indexing.
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Diffusion LM matches AR performance on medical VQA, runs 3.5-4.4x faster, and enables bidirectional infilling for interactive radiology report drafting.
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
citing papers explorer
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CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs
Medical VLMs frequently select negated options that contradict visible chest X-ray findings, achieving only ~30% accuracy on direct presence probes, but a post-hoc consistency verifier raises accuracy above 95%.
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Discrete Diffusion Language Models for Interactive Radiology Report Drafting
Diffusion LM matches AR performance on medical VQA, runs 3.5-4.4x faster, and enables bidirectional infilling for interactive radiology report drafting.
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UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.