Evaluation of Large Language Models in the Clinical Management of Patients With Upper Gastrointestinal Bleeding: Insights From Real-World Patient Data.

To evaluate large language models (LLMs) for pre-endoscopy (PE) risk stratification and prediction of endoscopic findings in upper gastrointestinal bleeding (UGIB), and compare their performance with clinical risk scores, conventional machine learning (ML) models, and hybrid approaches. This multicenter retrospective study included 384 patients with UGIB who underwent endoscopy. Five LLMs (GPT-5, Gemini-2.5-Flash, Llama 4, Grok, and DeepSeek R1) were tested using structured zero-shot prompts based on PE clinical and laboratory data. Their performance in identifying high-risk patients was compared with the Glasgow-Blatchford Score (GBS), AIMS65, PE Rockall score, and ML models. Hybrid LLM-score models were also assessed. Two gastroenterologists evaluated the quality of LLM-generated justifications. GBS showed the best discriminative performance among clinical scores (area under the receiver operating characteristic curve [AUROC] 0.73). Among LLMs, GPT-5 achieved the highest accuracy (0.66), while Grok showed the best-balanced performance (0.59; F1 0.47). Gemini-2.5-Flash had the highest sensitivity (0.89) but low specificity (0.21). Endoscopic prediction performance was modest, with Gemini-2.5-Flash achieving the highest exact-match accuracy (0.34) and micro-F1 (0.38). Hybrid models improved performance over standalone LLMs but did not outperform GBS alone (best: GBS+GPT-5, AUROC 0.670. LLMs showed higher numerical performance than conventional ML models, but a statistical comparison was not possible due to unavailable instance-level data. Grok received the highest human evaluation score for explanation quality. LLMs showed moderate performance in UGIB risk stratification and endoscopic prediction but were inferior to clinical scores, especially GBS. Hybrid models modestly improved over standalone LLMs but not GBS, supporting their use as adjunct tools rather than clinical decision-support systems. N/A.