Omar Mahmud, Nassar Salih, SharIf Kassem, Glicksberg Benjamin S, Nadkarni Girish N, Klang Eyal
Maccabi Health Services, Tel Aviv, Israel.
Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Front Med (Lausanne). 2025 Jan 22;11:1512824. doi: 10.3389/fmed.2024.1512824. eCollection 2024.
In 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.
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.
Our 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.
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.
近年来，随着大语言模型（LLM）的引入，自然语言处理（NLP）发生了显著变革。本综述更新了NLP和LLM在胃肠病学和肝病学中的应用及挑战。
在PROSPERO（CRD42024542275）注册并遵循PRISMA指南，我们在六个数据库中检索了2003年至2024年发表的相关研究，最终纳入57项研究。
我们对57项研究的综述指出，与前几年相比，2023 - 2024年相关出版物有所增加，这反映出对GPT - 3和GPT - 4等新型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持续改进。使用ROBINS - I、QUADAS - 2和PROBAST工具进行的偏倚风险评估证实了纳入研究的方法学稳健性。
NLP和LLMs可增强胃肠病学和肝病学的诊断与治疗。它们能够从非结构化医疗记录（如内镜检查报告和患者记录）中提取数据，以加强临床决策。尽管取得了这些进展，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实际应用价值。
