Mousavi Baigi Seyyedeh Fatemeh, Sarbaz Masoumeh, Darroudi Ali, Kemmak Fatemeh Dahmardeh, Aval Reyhane Norouzi, Kimiafar Khalil
Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran.
Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran.
J Med Signals Sens. 2025 Aug 6;15:22. doi: 10.4103/jmss.jmss_76_24. eCollection 2025.
Automated clinical coding, facilitated by artificial intelligence (AI) techniques like natural language processing and machine learning, has emerged as a promising approach to enhance coding efficiency and accuracy in healthcare. This review synthesizes current knowledge about AI-based automated coding of the International Classification of Diseases (ICD), with a focus on its challenges, benefits, and future research directions. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search was conducted across PubMed, Embase, Scopus, and Web of Science databases on January 1, 2024. Studies discussing challenges, advantages, and research gaps in AI-driven ICD coding were included. Out of 12,641 identified records, eight studies met the inclusion criteria. These studies highlighted six key challenges: extensive label space, imbalanced label distribution, lengthy documents, coding interpretability issues, ethical concerns, and lack of transparency. Ten major benefits of AI-based ICD coding were identified, including improved decision-making, data standardization, and increased coding accuracy. In addition, eight future directions were proposed, emphasizing interdisciplinary collaboration, transfer learning, transparency enhancement, and active learning techniques. Despite significant challenges, AI-based ICD coding holds substantial potential to revolutionize clinical coding by improving efficiency and accuracy. This review provides a comprehensive synthesis of current evidence and actionable insights for advancing research and practical implementation of automated ICD coding systems.
由自然语言处理和机器学习等人工智能（AI）技术推动的自动临床编码，已成为提高医疗保健领域编码效率和准确性的一种有前景的方法。本综述综合了当前关于基于AI的国际疾病分类（ICD）自动编码的知识，重点关注其挑战、益处及未来研究方向。按照系统评价和Meta分析的首选报告项目指南，于2024年1月1日在PubMed、Embase、Scopus和科学网数据库中进行了系统检索。纳入了讨论AI驱动的ICD编码中的挑战、优势和研究空白的研究。在12641条检索到的记录中，有8项研究符合纳入标准。这些研究突出了六个关键挑战：标签空间广泛、标签分布不均衡、文档冗长、编码可解释性问题、伦理问题以及缺乏透明度。确定了基于AI的ICD编码的十大主要益处，包括改善决策、数据标准化和提高编码准确性。此外，还提出了八个未来方向，强调跨学科合作、迁移学习、增强透明度和主动学习技术。尽管存在重大挑战，但基于AI的ICD编码在提高效率和准确性方面具有彻底改变临床编码的巨大潜力。本综述为推进自动ICD编码系统的研究和实际应用提供了当前证据的全面综合及可操作的见解。
