Performance Assessment of Large Language Models in Medical Consultation: Comparative Study.

IF 3.1 3区医学 Q2 MEDICAL INFORMATICS JMIR Medical Informatics Pub Date : 2025-02-12 DOI:10.2196/64318

Sujeong Seo, Kyuli Kim, Heyoung Yang

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引用次数: 0

Abstract

Background: The recent introduction of generative artificial intelligence (AI) as an interactive consultant has sparked interest in evaluating its applicability in medical discussions and consultations, particularly within the domain of depression.

Objective: This study evaluates the capability of large language models (LLMs) in AI to generate responses to depression-related queries.

Methods: Using the PubMedQA and QuoraQA data sets, we compared various LLMs, including BioGPT, PMC-LLaMA, GPT-3.5, and Llama2, and measured the similarity between the generated and original answers.

Results: The latest general LLMs, GPT-3.5 and Llama2, exhibited superior performance, particularly in generating responses to medical inquiries from the PubMedQA data set.

Conclusions: Considering the rapid advancements in LLM development in recent years, it is hypothesized that version upgrades of general LLMs offer greater potential for enhancing their ability to generate "knowledge text" in the biomedical domain compared with fine-tuning for the biomedical field. These findings are expected to contribute significantly to the evolution of AI-based medical counseling systems.

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来源期刊

JMIR Medical Informatics Medicine-Health Informatics

CiteScore

7.90

自引率

3.10%

发文量

173

审稿时长

12 weeks

期刊介绍： JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.