Data Sheet 1_Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.pdf

Background and aimIn the last years, natural language processing (NLP) has transformed significantly with the introduction of large language models (LLM). This review updates on NLP and LLM applications and challenges in gastroenterology and hepatology.
MethodsRegistered with PROSPERO (CRD42024542275) and adhering to PRISMA guidelines, we searched six databases for relevant studies published from 2003 to 2024, ultimately including 57 studies.
ResultsOur review of 57 studies notes an increase in relevant publications in 2023–2024 compared to previous years, reflecting growing interest in newer models such as GPT-3 and GPT-4. The results demonstrate that NLP models have enhanced data extraction from electronic health records and other unstructured medical data sources. Key findings include high precision in identifying disease characteristics from unstructured reports and ongoing improvement in clinical decision-making. Risk of bias assessments using ROBINS-I, QUADAS-2, and PROBAST tools confirmed the methodological robustness of the included studies.
ConclusionNLP and LLMs can enhance diagnosis and treatment in gastroenterology and hepatology. They enable extraction of data from unstructured medical records, such as endoscopy reports and patient notes, and for enhancing clinical decision-making. Despite these advancements, integrating these tools into routine practice is still challenging. Future work should prospectively demonstrate real-world value.

背景与目的 近年来，随着大语言模型（Large Language Model，LLM）的问世，自然语言处理（Natural Language Processing，NLP）领域发生了显著变革。本综述旨在梳理自然语言处理与大语言模型在胃肠病学和肝病学领域的应用现状及面临的挑战。
方法 本研究已在PROSPERO平台注册（注册号：CRD42024542275），并严格遵循PRISMA声明规范。我们检索了6个数据库中2003年至2024年发表的相关研究，最终纳入57项符合标准的研究。
结果 对纳入的57项研究的分析显示，2023至2024年相关研究成果的发表数量较往年有所上升，反映出学界对GPT-3、GPT-4等新型大语言模型的关注度日益提升。研究结果表明，自然语言处理模型可有效增强对电子健康档案及其他非结构化医疗数据源的信息提取能力。核心研究结果包括：从非结构化医疗报告中识别疾病特征时可达到较高精度，且临床决策辅助性能仍在持续优化。采用ROBINS-I、QUADAS-2及PROBAST工具开展的偏倚风险评估结果证实，本次纳入的所有研究均具备较好的方法学可靠性。
结论 自然语言处理与大语言模型可有效提升胃肠病学和肝病学的诊疗水平。此类工具可实现从内镜报告、患者病程记录等非结构化医疗记录中提取数据，并辅助优化临床决策流程。尽管取得了上述进展，但将此类工具整合入临床常规工作流程仍存在诸多挑战。未来研究应通过前瞻性研究验证其在真实临床场景中的应用价值。