ArticleScientific data2026
Towards Automated Reporting: A Bronchoscopy Report Dataset for Enhancing Multimodality Large Language Models.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness, Safety, and Workflow Burden of Large Language Model-Based Medical Report Generation: Systematic Review.Journal of medical Internet research · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bronchoscopy examination is essential for diagnosing and managing respiratory diseases. While Multimodality Large Language Models (MLLMs) can enhance the efficiency and accuracy of medical report writing, existing datasets lack descriptive and comprehensive annotations for complex cases, hindering their ability to facilitate adequate learning of image-report relationships. To address this problem, we introduce BERD, a Bronchoscopy Examination Report Dataset, which includes 3,692 bronchoscopy examination reports. Among these reports, 6,330 representative images are annotated with single-image text descriptions and classification labels. BERD emphasizes the provision of versatile and detailed descriptions of findings. All these reports and annotations were performed by experienced clinicians specializing in bronchoscopy. Furthermore, experimental results show that fine-tuning state-of-the-art MLLMs on BERD significantly improves their ability to generate accurate and comprehensive reports, advancing AI applications in bronchoscopy.
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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 Socratic graph.