Using a Diverse Test Suite to Assess Large Language Models on Fast Health Care Interoperability Resources Knowledge: Comparative Analysis.

ABSTRACT:

Background

Recent natural language processing breakthroughs, particularly with the emergence of large language models (LLMs), have demonstrated remarkable capabilities on general knowledge benchmarks. However, there is limited data on the performance and understanding of these models in relation to the Fast Healthcare Interoperability Resources (FHIR) standard. The complexity and specialized nature of FHIR present challenges for LLMs, which are typically trained on broad datasets and may have a limited understanding of the nuances required for domain-specific tasks. Improving health data interoperability can greatly benefit the use of clinical data and interaction with electronic health records.

Objective

This study presents the Fast Healthcare Interoperability Resources (FH

SUBMITTER: Idrissi-Yaghir A 

PROVIDER: S-EPMC12360669 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature