ArticleProceedings of the conference. Association for Computational Linguistics. Meeting2025
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning.
Article in Proceedings of the conference. Association for Computational Linguistics. Meeting, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Enhancing Medical Large Vision-Language Models via Alignment Distillation.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2026Article
- ClinVLA: an image-text retrieval method for promoting hospital diagnosis data analysis and patient health prediction.Frontiers in physiology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Medical Large Vision-Language Models (Med-LVLMs) often exhibit suboptimal attention distribution on visual inputs, leading to hallucinated or inaccurate outputs. Existing mitigation methods primarily rely on inference-time interventions, which are limited in attention adaptation or require additional supervision. To address this, we propose A
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.