GutGPT: A multidimensional knowledge-enhanced large language model for gastrointestinal medicine.

Background: Gastrointestinal (GI) diseases are common, chronic conditions that require personalized, long-term management, placing a heavy burden on traditional healthcare systems. While large language models (LLMs) offer potential for supporting patient care with personalized and empathetic guidance, existing models often lack domain-specific knowledge in GI diseases and suffer from issues like slow convergence and overfitting.

Methodology: We first construct a high-quality GI disease QA dataset comprising 191,615 entries from diverse sources: real-world doctor-patient dialogues, medical knowledge graphs, medical guidelines, and Chinese medical licensing exam data.

Automatic detection of small bowel lesions with different bleeding risks based on deep learning models.

Background: Deep learning provides an efficient automatic image recognition method for small bowel (SB) capsule endoscopy (CE) that can assist physicians in diagnosis. However, the existing deep learning models present some unresolved challenges.

Aim: To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks, and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.