Aug 2026· Frontiers in Digital Health· Vol 8· 0 citations· 70 references
Medicine
TL;DR
AI-based automatic ICD-10 coding is a promising approach for clinical coding support, and future research should prioritize rare-label imbalance, reproducibility, explainability, and validation across diverse clinical settings.
Abstract
Background ICD-10 coding is an essential process in healthcare systems that supports clinical management, reimbursement, and health data analytics. However, the complexity of its hierarchical structure and the large number of available codes make manual coding limited in terms of time, cost, and consistency. Despite growing research in this area, evidence remains fragmented, particularly regarding real-world implementation readiness. Objective To review and synthesize existing knowledge on algorithms, datasets, evaluation methods, and real-world implementation readiness of automatic ICD-10 coding systems. Methods Eligible studies were original research articles, preprints, or conference papers published in English between January 1, 2020 and December 31, 2025, and retrieved from seven academic databases: Scopus, PubMed, Web of Science, IEEE Xplore, ACM Digital Library, arXiv, and Google Scholar. Studies were included if they investigated automatic ICD-10 coding from clinical text using machine learning, deep learning, transformer-based, or large language model (LLM) approaches. Methodological quality was assessed using a research-question-driven appraisal framework. This systematic review followed PRISMA 2020 guidance and was preregistered in the Open Science Framework (OSF) at https://osf.io/cegqk. Results A total of 257 records were identified, of which 24 studies met the inclusion criteria and contributed 296 experimental evaluations overall. Study quality was high in 7 studies, moderate in 8, and limited by technical or methodological concerns in 9. Hybrid deep learning (Hybrid DL) was most often used as the main automated coding approach, while machine learning (ML) and rule-based approaches were mainly used as baselines. F1-macro was consistently lower than F1-micro among studies reporting both metrics. Hybrid DL showed the most stable performance under all-code or full-code evaluation, while AI model performance varied by the documents-per-label (D/L) ratio. Discussion The evidence indicates continued technical progress, particularly through Hybrid DL and transformer-based approaches, while LLM-based methods remain emerging and less consistently effective for structured multi-label coding. The observed D/L–performance relationship suggested that AI model selection should consider dataset structure and label support, in addition to algorithmic complexity. Conclusion AI-based automatic ICD-10 coding is a promising approach for clinical coding support. Future research should prioritize rare-label imbalance, reproducibility, explainability, and validation across diverse clinical settings. Systematic Review Registration https://osf.io/cegqk.
This work proposes Knowledge-Guided Reasoning over Clinical Evidence with LLMs (KREL), a framework that leverages LLMs for clinical text understanding and reasoning while integrating external ICD coding guidelines as structured knowledge, and enables tight coupling between domain knowledge and LLM reasoning.
Xubin Chen, Yipeng Zhou, Wenxin Sun et al.· 0 citations
The International Classification of Diseases (ICD) coding system is central to reimbursement, registry construction, and secondary analysis, yet automated inpatient International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis coding from discharge documentation remains difficult because clinical notes are long, diagnoses are multi-label, and supporting evidence is often scattered across multiple note sections. Existing systems also provide limited justification for why a code was assigned. This study develops and evaluates a Large Language Model (LLM)-based approach for accurate and evidence-grounded ICD-10-CM coding. Our framework combines three components: a two-stage Supervised Fine-Tuning (SFT) strategy that first aligns the base model with medical instructions and then specializes it for ICD coding, an ICD-aligned Reinforcement Learning (RL) algorithm with reward and advantage functions tailored to the hierarchical structure of ICD codes, and a Retrieval-Augmented Generation (RAG)-based verifier that removes predictions lacking external support. On the Medical Information Mart for Intensive Care IV (MIMIC-IV) benchmark, two-stage SFT improves Micro-F1 from 14.4% to 64.0%, ICD-specific RL further raises it to 66.7%, and the verifier reaches 67.5%, exceeding the strongest previously reported MIMIC-IV result by 8.8 points. These results show that domain adaptation, task-aligned optimization, and evidence-guided verification can improve both coding accuracy and the auditability of model outputs. The resulting system is intended as a decision-support tool for professional coders rather than a fully autonomous deployment.
Jian-Hua Qiu, Zhen Qin, Kai-Li Ma et al.· PeerJ Computer Science· 0 citations
The increasing complexity of medical diagnosis and billing necessitates improving International Classification of Diseases (ICD) coding practices. It also requires partnerships between academia and industry to support rigor and implementation in real-world settings. We conducted an academic-industry collaboration involving four institutions to explore pretrained language models for artificial intelligence (AI)-driven ICD-10 coding in cancer-specific populations. We created a cancer-focused dataset comprising 101,224 clinical notes and 36,040 ICD code assignments across five note types, reflecting real-world scenarios. By fine-tuning and evaluating two existing pretrained language-model approaches designed to process long contexts, PLM-ICD and KEPTLongformer, we developed an institutional benchmark for oncology ICD-10 coding using 20 selected three-character ICD-10 parent codes. The best-performing model achieved an F1-macro of 0.768 and an F1-micro of 0.792 on the test set. Our feasibility analysis showed that integrating these models into clinical workflows could potentially reduce coding time by approximately 10 minutes per case, with a GPU runtime of 7 seconds. Additionally, an exploratory clinical-coder evaluation of attention-based model interpretability showed that 13 of 20 predictions contained high-attention tokens aligned with the corresponding ICD code descriptions; coders rated these tokens as "very helpful" for code assignment. The results demonstrate the potential of AI-driven coding support systems within clinical workflows.
M. Zitu, Dana Rollison, Kedar S. Kulkarni et al.· IEEE/ACM International Confe...· 0 citations
A systematic evaluation framework for procedure-code recommendation is established and practical requirements for future systems that are accurate, review-efficient, and grounded in clinical evidence are identified.
Q. Song, C. Ni, W. Liu et al.· medRxiv· 0 citations
The findings support the feasibility of applying LLM-based natural language processing tools in resource-limited, non-English healthcare settings and should assess emerging high-parameter models and explore additional clinical domains.
Breno Gabriel Araújo Sampaio de Jesus, Tomaz Castrillon Figueiredo, Clariele de Almeida Pereira et al.· Cadernos de Saúde Pública· 1 citation
Artificial intelligence (AI) is reshaping the way medical information is processed and has shown considerable potential in medical record coding and diagnosis-related group (DRG) management. Traditional medical record management mainly relies on manual coding and rule-based matching, which is often limited by low efficiency, heavy workload, and insufficient consistency, making it difficult to meet the demands of large-scale healthcare data processing. In recent years, machine learning, deep learning, and natural language processing (NLP) have been increasingly applied to automated coding, clinical information extraction, and medical record quality control, and have gradually expanded to DRG grouping prediction, risk control, and hospital operation management. Large language models (LLMs) offer new opportunities for complex medical text understanding and candidate decision support; however, their application in medical record coding and DRG management remains at an early stage of validation and exploration. At present, this field still faces several challenges, including data heterogeneity, limited model interpretability, privacy and security concerns, and insufficient cross-institutional generalizability. Future efforts should focus on standardized validation, multimodal data integration, and human–AI collaboration mechanisms to promote the robust development of intelligent medical record management systems.
Dandan Ji, Mingkui Huang, Lie-zhi Wang· Frontiers in Medicine· 0 citations
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