Artificial Intelligence-based Automated International Classification of Diseases Coding: A Systematic Review

Abstract

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.