ArticleNPJ digital medicine2026
Domain specific multimodal large language model for automated endoscopy reporting with multicenter prospective validation.
Article in NPJ digital medicine, 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
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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
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
14 authors.
Funding
Abstract
Accurate endoscopy reports are crucial for the diagnosis and management of patients with upper gastrointestinal (UGI) diseases, yet errors and omissions are common. The preparation of routine reports for common diseases is labor-intensive and time-consuming. To address this, here we developed Report-Angel, an integrated AI system based on a multi-modal large language model (MLLM) and conventional deep learning models and trained it on 20,617 image-text pairs to automatically generate detailed draft reports for UGI endoscopy. Report-Angel achieved a clinically acceptable report rate of 79.3% (95% CI: 74.4-83.5%) in the prospective internal and 83.3% (95% CI: 78.7-87.3%) in the prospective external cohort. At the case level, Report-Angel achieved a report completeness of 88.51% (95% CI: 84.64-92.38%) and a report accuracy of 78.93% (95% CI: 73.98-83.88%), with average processing time of 1.5 s per lesion in the internal prospective video dataset. Lesion-level reporting accuracies were 91.92% (95% CI: 90.58-93.25%), 89.07% (95% CI: 87.57-90.57%), and 83.94% (95% CI: 81.58-86.31%) on retrospective image and prospective single- and multi-center video datasets, respectively. Report-Angel generates expert-level draft endoscopy reports and demonstrates robust generalizability. By providing reliable foundation draft reports, this system has the potential to effectively standardize reporting and reduce endoscopists' workloads.
Identifiers
What OpenQuestion holds
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.