Evaluating large language models' performance in answering common questions on drug-induced liver injury.

ABSTRACT:

Background & aims

Drug-induced liver injury (DILI) is a complex condition often linked to medication behaviors, with patient education having a crucial role in optimizing outcomes. Large language models (LLMs) could serve as promising tools for scalable patient support, but their utility remains unclear. This study systematically evaluated the capability of six popular open- and closed-source LLMs in addressing common DILI-related queries, focusing on patient-centered education.

Methods

Twenty-eight frequently asked DILI questions were collected with input from hepatologists and patients (n = 15), and categorized into six clinical domains. Responses from six LLMs (GPT-4, GPT-3.5, Claude-2, Claude-1.3, Gemini, and LLaMA-3.1-405B) were anonymized, randomized, and independently

SUBMITTER: Dong Y 

PROVIDER: S-EPMC12657742 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature