arXiv:2601.15408v2 Announce Type: replace-cross Abstract: Medical vision-language models can automate the generation of radiology reports but struggle with accurate visual grounding and factual consistency. Existing models often misalign textual findings with visual evidence, leading to unreliable or weakly grounded predictions. We present CURE, an error-aware curriculum learning framework that improves grounding and report quality without any additional data. CURE fine-tunes a multimodal instructional model on phrase grounding, grounded report generation, and anatomy-grounded report generatio
This development emerges as medical AI models face increasing scrutiny over reliability and accuracy, pushing research towards more robust and interpretable solutions.
Improved medical vision-language models, like CURE, can significantly enhance diagnostic report generation, reducing errors and improving clinical efficiency, which has direct implications for healthcare costs and patient outcomes.
The focus shifts towards curriculum learning and error-awareness in medical AI, emphasizing grounded and consistent predictions rather than just automated output.
- · Healthcare providers
- · Medical AI developers
- · Patients
- · Radiology departments
- · Companies relying on ungrounded AI models
- · Legacy diagnostic systems
More accurate and reliable AI-generated radiology reports become available for clinical use.
Increased trust in AI-powered diagnostics accelerates their adoption across the medical field.
This could lead to a restructuring of diagnostic workflows, with AI taking a more central role in initial report drafting and physician review.
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Read at arXiv cs.LG