Emerging Applications of NLP and Large Language Models in Gastroenterology and Hepatology: A Systematic Review

Mahmud Omar, Kassem Sharif, Benjamin S Glicksberg, Girish Nadkarni, Eyal Klang
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Abstract

Background and Aim: In the last two 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. Methods: Registered with PROSPERO (CRD42024542275) and adhering to PRISMA guidelines, we searched six databases for relevant studies published from 2003 to 2024, ultimately including 57 studies. Results: Our review 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. Conclusion: NLP 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. Keywords: Natural Language Processing, Large Language Models, Gastroenterology, Hepatology, Electronic Health Records.
NLP 和大型语言模型在胃肠病学和肝病学中的新兴应用:系统综述
背景和目的:在过去两年中,随着大型语言模型(LLM)的引入,自然语言处理(NLP)发生了巨大变化。这篇综述更新了 NLP 和 LLM 在胃肠病学和肝病学中的应用和挑战:我们在PROSPERO(CRD42024542275)注册并遵守PRISMA指南,在六个数据库中检索了2003年至2024年发表的相关研究,最终包括57项研究:我们的审查结果表明,与前几年相比,2023-2024 年发表的相关论文有所增加,这反映出人们对 GPT-3 和 GPT-4 等较新模型的兴趣日益浓厚。结果表明,NLP 模型增强了从电子健康记录和其他非结构化医疗数据源中提取数据的能力。主要发现包括从非结构化报告中识别疾病特征的高精度以及临床决策的不断改进。使用 ROBINS-I、QUADAS-2 和 PROBAST 工具进行的偏倚风险评估证实了所纳入研究在方法上的稳健性:结论:NLP 和 LLM 可提高胃肠病学和肝病学的诊断和治疗水平。它们可以从内窥镜检查报告和病人笔记等非结构化医疗记录中提取数据,并用于加强临床决策。尽管取得了这些进步,但将这些工具整合到常规实践中仍具有挑战性。未来的工作应前瞻性地证明其在现实世界中的价值:自然语言处理、大型语言模型、消化内科、肝病科、电子病历。
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