Large language models as medical code selectors: a benchmark using the International Classification of Primary Care.

ABSTRACT:

Objectives

Medical coding structures health-care data for research, quality monitoring, and policy. This study assesses the potential of large language models (LLMs) to assign International Classification of Primary Care, 2nd edition (ICPC-2) codes using the output of a domain-specific search engine.

Materials and methods

A dataset of 437 Brazilian Portuguese clinical expressions, each annotated with ICPC-2 codes, was used. A semantic search engine (OpenAI's text-embedding-3-large) retrieved candidates from 73 563 labeled concepts. Thirty-three LLMs were prompted with each query and retrieved results to select the best-matching ICPC-2 code. Performance was evaluated using F1-score, along with token usage, cost, response time, and format adherence.

Results

Twenty-eight

SUBMITTER: Anjos de Almeida V 

PROVIDER: S-EPMC12924630 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature