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Journal of Medical Signals and Sensors، جلد ۱۵، شماره ۸، صفحات ۱۰-۴۱۰۳
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عنوان انگلیسی Artificial Intelligence-based Automated International Classification of Diseases Coding: A Systematic Review
چکیده انگلیسی مقاله Abstract 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.
کلیدواژه‌های انگلیسی مقاله Artificial intelligence,autocoding,automatic coding,International Classification of Diseases
نویسندگان مقاله | Seyyedeh Fatemeh Mousavi Baigi
Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran


| Masoumeh Sarbaz
1.Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran 2.Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran


| Ali Darroudi
1.Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran 2.Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran


| Fatemeh Dahmardeh Kemmak
1.Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran 2.Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran


| Reyhane Norouzi Aval
Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, Iran


| Khalil Kimiafar


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/761
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نوع مقاله منتشر شده Review Articles
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