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

Sujeong Seo^1,2, Kyuli Kim¹, Heyoung Yang¹

¹Future Technology Analysis Center, Korea Institute of Science and Technology Information, Seoul, Republic of Korea.

JMIR Medical Informatics

|January 7, 2025

Summary

This summary is machine-generated.

Generative artificial intelligence (AI) shows promise in mental health. Latest large language models (LLMs) like GPT-3.5 and Llama2 excel at answering depression-related queries, advancing AI in medical consultations.

Keywords:

artificial intelligence biomedical depression large language model similarity measurement text validity

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Area of Science:

Artificial Intelligence
Medical Informatics
Computational Linguistics

Background:

Generative artificial intelligence (AI) is emerging as an interactive consultant.
Its application in medical discussions, especially for depression, is of significant interest.

Purpose of the Study:

To evaluate the capability of large language models (LLMs) in AI to generate responses to depression-related queries.
To assess the performance of different LLMs in the biomedical domain.

Main Methods:

Comparison of multiple LLMs (BioGPT, PMC-LLaMA, GPT-3.5, Llama2) using PubMedQA and QuoraQA datasets.
Measurement of response similarity between generated and original answers.

Main Results:

General LLMs, specifically GPT-3.5 and Llama2, demonstrated superior performance.
These models excelled in generating accurate responses to medical inquiries from the PubMedQA dataset.

Conclusions:

Upgraded general LLMs may outperform specialized models for generating biomedical knowledge text.
Findings support the advancement of AI-based medical counseling systems.