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Gaze-supervised AI enhances chest X-ray diagnosis and report generation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel two-stage multimodal framework for interpreting chest X-rays, integrating radiologist eye-tracking data to improve diagnostic accuracy and report generation. The first stage employs a gaze-token classifier that fuses image patches, transcriptions, and radiologist fixations, with added fixation supervision boosting AUC by 4.4% and F1 by 13.3%. The second stage translates these predictions into region-specific diagnostic sentences, extracting confidence-weighted keywords and using a prompted large language model to enhance clinical-term scores. This approach offers a new benchmark for interpretable, gaze-aware chest X-ray analysis. AI

IMPACT Enhances diagnostic accuracy and transparency in medical imaging analysis, potentially improving patient care.

RANK_REASON Research paper detailing a novel multimodal learning framework for medical imaging analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Gaze-supervised AI enhances chest X-ray diagnosis and report generation

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Research paper detailing a novel multimodal learning framework for medical imaging analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tanjim Islam Riju, Shuchismita Anwar, Saman Sarker Joy, Farig Sadeque, Swakkhar Shatabda ·

    Eyes on the Image: Gaze Supervised Multimodal Learning for Chest X-ray Diagnosis and Report Generation

    arXiv:2508.13068v2 Announce Type: replace-cross Abstract: Medical vision-language models still struggle to match radiologists' attention and to verbalize findings with explicit spatial grounding. We address this gap with a two-stage multimodal framework for chest X-ray interpreta…

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