Large language models (LLMs) are transforming the medical landscape by enhancing access to information, diagnostics, treatment customization, and medical education, especially in areas like Gastroenterology. LLMs utilize extensive medical data to improve decision-making, leading to better patient outcomes and personalized medicine. These models are instrumental in interpreting medical literature and synthesizing patient data, facilitating real-time knowledge for physicians and supporting educational pursuits in medicine. Despite their potential, the complete integration of LLMs in real-life remains ongoing, particularly requiring further study and regulation. This review highlights the existing evidence supporting LLMs’ use in Gastroenterology, addressing both their potential and limitations. Recent studies demonstrate LLMs’ ability to answer questions from physicians and patients accurately. Specific applications in this field, such as colonoscopy, screening for colorectal cancer, and hepatobiliary and inflammatory bowel diseases, underscore LLMs’ promise in improving the communication and understanding of complex medical scenarios. Moreover, the review discusses LLMs’ efficacy in clinical contexts, providing guideline-based recommendations and supporting decision-making processes. Despite these advancements, challenges such as data completeness, reference suitability, variability in response accuracy, dependency on input phrasing, and a lack of patient-generated questions underscore limitations in reproducibility and generalizability. The effective integration of LLMs into medical practice demands refinement tailored to specific medical contexts and guidelines. Overall, while LLMs hold significant potential in transforming medical practice, ongoing development and contextual training are essential to fully realize their benefits.
大型语言模型（LLM）正在通过提升信息获取、辅助诊断、优化治疗方案定制及推动医学教育革新医疗领域，尤其在消化病学等专科中表现突出。该模型通过整合海量医疗数据提升临床决策质量，从而改善患者预后并促进个体化医疗发展。LLM在解析医学文献与整合患者数据方面发挥关键作用，既能为医生提供实时知识支持，又能助力医学教育发展。尽管潜力巨大，LLM在现实医疗场景中的全面整合仍处于推进阶段，尤其需要进一步的研究探索与规范监管。本文综述聚焦消化病学领域，系统评述支持LLM应用的科学证据，辩证分析其优势与局限。最新研究表明，LLM能够准确回应医患双方提出的专业问题。在结肠镜检查、结直肠癌筛查、肝胆疾病及炎症性肠病等具体场景中的应用，彰显了LLM在改善复杂医疗情境沟通与理解方面的价值。此外，本文探讨了LLM在临床实践中的效能，包括基于指南的诊疗建议生成和决策支持功能。尽管取得进展，但数据完整性不足、参考文献适配性有限、回答准确性存在波动、输出结果受提问表述方式影响以及缺乏患者视角问题库等挑战，制约了其应用的可重复性与普适性。要将LLM有效整合到医疗实践中，需要针对特定医疗场景和指南进行精细化调整。总体而言，虽然LLM在变革医疗实践方面具有显著潜力，但持续的技术优化与场景化训练是实现其全面效益的必要条件。
The Application of Large Language Models in Gastroenterology: A Review of the Literature
肿瘤(癌症)患者之家