Articlenpj health systems2025
Enhancing medical coding efficiency through domain-specific fine-tuned large language models.
Article in npj health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- HieraMed: A hierarchical representation framework for next-visit diagnosis prediction from longitudinal electronic health records.MethodsX · 2026Article
- Augmenting structured diagnoses through effective use of pre-trained large language models on clinical notes.JAMIA open · 2026Article
- RadCoT: a Radiological Chain-of-Thought framework for enhanced error detection in radiology reports.European radiology experimental · 2026Article
- Sequential multi-site fine-tuning for incremental deployment of large language models for mobility functional status extraction.JAMIA open · 2026Article
- Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Large Language Models for Postoperative Decision Support: Comparative Analysis.Journal of medical Internet research · 2026Article
- Artificial Intelligence and Clinician Burnout in the United States: A Narrative Review.Cureus · 2026Review
- Assessing Eligibility for Anticancer Drug Health Insurance Reimbursement Using Large Language Models: Benchmark Development and Comparative Study.Journal of medical Internet research · 2026Article
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Optimizing GPT-5 for Operation-Procedure-Code-Extraction from Operative Reports in Meningioma Surgery: Feasibility and Comparison of Context-Enhancements.Applied clinical informatics · 2026Article
- Evaluating real-world deployment of an HL7-CDA-aligned LLM for ICD-10-CM coding.NPJ digital medicine · 2026Article
- Evaluation of Large Language Models for Automated SimpleFoot & ankle orthopaedics · 2026Article
- Integrating Fine-Tuning and Retrieval-Augmented Generation for Healthcare AI Systems: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Artificial intelligence-assisted medical coding and DRG management: current applications, challenges, and future perspectives.Frontiers in medicine · 2026Review
- Benchmarking Foundation Models with Multimodal Public Electronic Health Records.IEEE journal of biomedical and health informatics · 2025Article
- Response to: Beyond PlasticSurgeryGPT: The Imperative for Reasoning-Enhanced LLMs in Plastic Surgery.Aesthetic surgery journal · 2025Article
- Large Language Models for Psychiatric Phenotype Extraction from Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Medical coding is essential for healthcare operations yet remains predominantly manual, error-prone (up to 20%), and costly (up to $18.2 billion annually). Although large language models (LLMs) have shown promise in natural language processing, their application to medical coding has produced limited accuracy. In this study, we evaluated whether fine-tuning LLMs with specialized ICD-10 knowledge can automate code generation across clinical documentation. We adopted a two-phase approach: initial fine-tuning using 74,260 ICD-10 code-description pairs, followed by enhanced training to address linguistic and lexical variations. Evaluations using a proprietary model (GPT-4o mini) on a cloud platform and an open-source model (Llama) on local GPUs demonstrated that initial fine-tuning increased exact matching from <1% to 97%, while enhanced fine-tuning further improved performance in complex scenarios, with real-world clinical notes achieving 69.20% exact match and 87.16% category match. These findings indicate that domain-specific fine-tuned LLMs can reduce manual burdens and improve reliability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.