| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Observational Study |
| Article Title |
Large language models with supervised fine-tuning for inflammatory bowel disease diagnosis and prognosis
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Shi-Ming Zhou, Ming-Jun Zhang, Shu-Lin Zhang, Tao Chen, Zheng-Jie Wei, Hai-Bin Dong, Qi Cui, Meng Li, Dan Wang, Hui-Chao Wang, Yi-Ru Guan, Hai-Hua Yang, Bahabaike Jiangtulu, Bo Cui, Qian Xu, Yi Zhao and Xuan Jiang |
| Funding Agency and Grant Number |
|
| Corresponding Author |
Xuan Jiang, MD, Department of Gastroenterology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, No. 168 Litang Road, Beijing 102218, China. jxa01998@btch.edu.cn |
| Key Words |
Large language models; Inflammatory bowel disease; Disease screening; Supervised fine-tuning; Diagnosis |
| Core Tip |
This study constructed a specialized inflammatory bowel disease-large language model (LLM) using supervised fine-tuning (SFT) on a limited number of high-quality data from real-world electronic health records. In a head-to-head comparison with resident physicians during simulated clinical interactions, the model excelled in humanistic care, clinical judgment, and organizational effectiveness, but demonstrated a significant deficit in diagnostic accuracy. Notably, no artificial intelligence hallucinations were observed, attributed to rigorous structured training and a conservative learning rate. These findings establish SFT-trained LLMs as valuable clinical adjuncts for enhancing patient interaction in resource-constrained settings, although complex diagnostic reasoning still mandates human oversight. |
| Citation |
Zhou SM, Zhang MJ, Zhang SL, Chen T, Wei ZJ, Dong HB, Cui Q, Li M, Wang D, Wang HC, Guan YR, Yang HH, Jiangtulu B, Cui B, Xu Q, Zhao Y, Jiang X. Large language models with supervised fine-tuning for inflammatory bowel disease diagnosis and prognosis. World J Gastroenterol 2026; In press
|
| PDF |
123087-in-press.pdf
|
