Observational studyWorld journal of gastroenterology2025
DeepGut: A collaborative multimodal large language model framework for digestive disease assisted diagnosis and treatment.
Observational study in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Gastrointestinal endoscopy for physiological assessment in functional gastrointestinal disorders: a perspective.Frontiers in medicine · 2026Article
- Building novel LLM-enabled explainable ensemble transformer models combining endoscopic and CT images for discriminating the different grades of gastrointestinal cancers.Frontiers in medicine · 2026Article
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Authors and funding
9 authors.
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Abstract
backgroundGastrointestinal diseases have complex etiologies and clinical presentations. An accurate diagnosis requires physicians to integrate diverse information, including medical history, laboratory test results, and imaging findings. Existing artificial intelligence-assisted diagnostic tools are limited to single-modality information, resulting in recommendations that are often incomplete and may be associated with clinical or legal risks.
aimTo develop and evaluate a collaborative multimodal large language model (LLM) framework for clinical decision-making in digestive diseases.
methodsIn this observational study, DeepGut, a multimodal LLM collaborative diagnostic framework, was developed to integrate four distinct large models into a four-tiered structure. The framework sequentially accomplishes multimodal information extraction, logical "chain" construction, diagnostic and treatment suggestion generation, and risk analysis. The model was evaluated using objective metrics, which assess the reliability and comprehensiveness of model-generated results, and subjective expert opinions, which examine the effectiveness of the framework in assisting physicians.
resultsThe diagnostic and treatment recommendations generated by the DeepGut framework achieved exceptional performance, with a diagnostic accuracy of 97.8%, diagnostic completeness of 93.9%, treatment plan accuracy of 95.2%, and treatment plan completeness of 98.0%, significantly surpassing the capabilities of single-modal LLM-based diagnostic tools. Experts evaluating the framework commended the completeness, relevance, and logical coherence of its outputs. However, the collaborative multimodal LLM approach resulted in increased input and output token counts, leading to higher computational costs and extended diagnostic times.
conclusionThe framework achieves successful integration of multimodal diagnostic data, demonstrating enhanced performance enabled by multimodal LLM collaboration, which opens new horizons for the clinical application of artificial intelligence-assisted technology.
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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.