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Related Experiment Video

Updated: May 24, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Improving Meditron for Medical Coding Through Fine-Tuning: A Comparative Evaluation Against GPT-4.

Coralie Galland-Decker¹, Muaziza Ursenbacher¹, Christophe Nunes¹

¹Medical informatics, Lausanne University Hospital, Switzerland.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) show potential for medical coding, but accuracy varies. GPT-4 and Meditron-CHUV performed differently across specialties, with SNOMED-CT codes often invalid, highlighting the need for hybrid approaches.

Keywords:

Clinical Coding Emergency room visits GPT-4 ICD 10 Large Language Models SNOMED-CT

Related Experiment Videos

Last Updated: May 24, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Clinical Documentation

Background:

Medical coding is crucial for healthcare billing and clinical record-keeping but presents significant challenges.
Previous studies indicated limitations in large language model (LLM) accuracy for medical coding tasks.
The need for efficient and accurate diagnostic coding is paramount in modern healthcare systems.

Purpose of the Study:

To compare the performance of GPT-4 and a fine-tuned LLM (Meditron-CHUV) in generating International Classification of Diseases, 10th Revision (ICD-10) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) admission diagnoses from clinical anamnesis.
To evaluate clinician preference and performance variations across medical specialties.

Main Methods:

A comparative study involving GPT-4 and Meditron-CHUV, fine-tuned on clinical data from Lausanne University Hospital.
Generation of ICD-10 and SNOMED-CT diagnoses from 40 synthetic clinical vignettes based on anamnesis alone.
Blind evaluation of generated diagnoses by five clinicians, assessing overall preference and specialty-specific performance.

Main Results:

GPT-4 was generally preferred by clinicians, but Meditron-CHUV demonstrated superior performance in adult medicine.
Model performance varied significantly by medical specialty (p < 0.001), but not by the clinicians' professional background.
Despite plausible labels, SNOMED-CT codes generated by both models were systematically invalid.

Conclusions:

Current LLMs have limitations in generating accurate and valid medical codes, particularly for SNOMED-CT.
The choice of LLM for medical coding should consider the specific clinical specialty.
Hybrid approaches combining LLM capabilities with retrieval-augmented generation and ontologies are recommended for improved accuracy and validity. Real-world data validation is the next step.