Developing an ICD-10 Coding Assistant: Pilot Study Using RoBERTa and GPT-4 for Term Extraction and Description-Based Code Selection.

ABSTRACT:

Background

The International Classification of Diseases (ICD), developed by the World Health Organization, standardizes health condition coding to support health care policy, research, and billing, but artificial intelligence automation, while promising, still underperforms compared with human accuracy and lacks the explainability needed for adoption in medical settings.

Objective

The potential of large language models for assisting medical coders in the ICD-10 coding was explored through the development of a computer-assisted coding system. This study aimed to augment human coding by initially identifying lead terms and using retrieval-augmented generation (RAG)-based methods for computer-assisted coding enhancement.

Methods

The explainability dataset from the CodiEs

SUBMITTER: Puts S 

PROVIDER: S-EPMC11835781 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature