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DeepGut: A collaborative multimodal large language model framework for digestive disease assisted diagnosis and
Xiao-Han Wan1, Mei-Xia Liu1, Yan Zhang1
1Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan 250012, Shandong Province, China.
World Journal of Gastroenterology
|September 3, 2025
Summary
A new multimodal large language model (LLM) framework, DeepGut, enhances diagnostic accuracy for gastrointestinal diseases by integrating diverse patient data. This AI tool offers improved diagnostic and treatment recommendations, surpassing single-modality systems.
Area of Science:
- Artificial Intelligence in Medicine
- Digestive Disease Diagnostics
- Clinical Decision Support Systems
Background:
- Gastrointestinal diseases present complex diagnostic challenges requiring integration of varied patient data.
- Current AI diagnostic tools often rely on single data types, leading to incomplete recommendations and potential risks.
- Physicians need advanced tools to synthesize medical history, lab results, and imaging for accurate diagnosis.
Purpose of the Study:
- To develop and evaluate a collaborative multimodal large language model (LLM) framework for improved clinical decision-making in digestive diseases.
- To create an AI system capable of integrating diverse data sources for comprehensive diagnostic support.
- To assess the performance and reliability of a novel multimodal LLM in assisting physicians with gastrointestinal diagnoses.
Main Methods:
- Developed DeepGut, a four-tiered collaborative multimodal LLM framework integrating four large models.
- Implemented sequential processing for multimodal information extraction, logical chain construction, and diagnostic/treatment suggestion generation.
- Evaluated the framework using objective metrics for reliability and comprehensiveness, alongside subjective expert assessments.
Main Results:
- DeepGut achieved high performance: 97.8% diagnostic accuracy, 93.9% diagnostic completeness, 95.2% treatment plan accuracy, and 98.0% treatment plan completeness.
- The multimodal LLM collaboration significantly outperformed single-modal AI diagnostic tools.
- Expert reviewers praised the completeness, relevance, and logical coherence of the AI-generated outputs.
Conclusions:
- The DeepGut framework successfully integrates multimodal diagnostic data for enhanced AI-assisted clinical decision-making in digestive diseases.
- Multimodal LLM collaboration demonstrates significant potential for advancing artificial intelligence applications in healthcare.
- This approach opens new avenues for utilizing AI to support physicians in complex diagnostic scenarios.
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