ArticleJMIR medical informatics2025
Performance Assessment of Large Language Models in Medical Consultation: Comparative Study.
Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- An automatic consult reply system for therapeutic plasma exchange using retrieval-augmented generation.Vox sanguinis · 2026Article
- Self-regulating the use of large language models in clinical practice: a risk-stratified approach.BMJ health & care informatics · 2026Article
- Key concept learning for medical vision language model with reasoning capabilities.NPJ digital medicine · 2026Article
- Evaluation of large Language models on pediatric asthma: a comparative study of Claude3-Opus, Gemini 2.0, ChatGPT-4o, and DeepSeek-a cross-sectional questionnaire study.BMC medical informatics and decision making · 2026Article
- Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study.Journal of medical Internet research · 2025Article
- Leveraging large language models for automated depression screening.PLOS digital health · 2025Article
- Promoting trust and intention to adopt health information generated by ChatGPT among healthcare customers: An empirical study.Digital healthArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectiveThis study evaluates the capability of large language models (LLMs) in AI to generate responses to depression-related queries.
methodsUsing 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.
resultsThe latest general LLMs, GPT-3.5 and Llama2, exhibited superior performance, particularly in generating responses to medical inquiries from the PubMedQA data set.
conclusionsConsidering 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.