ArticleJournal of education and health promotion2024
A large language model in solving primary healthcare issues: A potential implication for remote healthcare and medical education.
Article in Journal of education and health promotion, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review.Journal of medical Internet research · 2025Pooled it
- Evidence, use cases, and implementation safeguards of large language models in primary care.Communications medicine · 2026Review
- Exploring the Role of Large Language Models in Primary Care: Qualitative Study of Physicians in the United States and the Netherlands.JMIR medical informatics · 2026Article
- Assessment of Generative AI Chatbots for Neonatal Diagnostic Reasoning: A Comparative Concordance and Diagnosis Acceptability Analysis.Indian journal of pediatrics · 2026Article
- Evaluating the effect of artificial intelligence-based oral health education model among 10 to 15-year-old children in Adivasi Ashram School of Central India - An interventional study.Journal of education and health promotion · 2026Article
- Digital support for the care of menopausal women in primary health care: Development and validation of an application.Journal of education and health promotion · 2026Article
- Empowering standardized residency training in China through large language models: problem analysis and solutions.Annals of medicine · 2025Article
- Evaluation of Three Large Language Models' Response Performances to Inquiries Regarding Post-Abortion Care in the Context of Chinese Language: A Comparative Analysis.Risk management and healthcare policy · 2025Article
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4 authors.
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Abstract
BACKGROUND AND
aimAccess to quality health care is essential, particularly in remote areas where the availability of healthcare professionals may be limited. The advancement of artificial intelligence (AI) and natural language processing (NLP) has led to the development of large language models (LLMs) that exhibit capabilities in understanding and generating human-like text. This study aimed to evaluate the performance of a LLM, ChatGPT, in addressing primary healthcare issues. MATERIALS AND
methodsThis study was conducted in May 2023 with ChatGPT May 12 version. A total of 30 multiple-choice questions (MCQs) related to primary health care were selected to test the proficiency of ChatGPT. These MCQs covered various topics commonly encountered in primary healthcare practice. ChatGPT answered the questions in two segments-one is choosing the single best answer of MCQ and another is supporting text for the answer. The answers to MCQs were compared with the predefined answer keys. The justifications of the answers were checked by two primary healthcare professionals on a 5-point Likert-type scale. The data were presented as number and percentage.
resultsAmong the 30 questions, ChatGPT provided correct responses for 28 yielding an accuracy of 93.33%. The mean score for explanation in supporting the answer was 4.58 ± 0.85. There was an inter-item correlation of 0.896, and the average measure intraclass correlation coefficient (ICC) was 0.94 (95% confidence interval 0.88-0.97) indicating a high level of interobserver agreement.
conclusionLLMs, such as ChatGPT, show promising potential in addressing primary healthcare issues. The high accuracy rate achieved by ChatGPT in answering primary healthcare-related MCQs underscores the value of these models as resources for patients and healthcare providers in remote healthcare settings. This can also help in self-directed learning by medical students.
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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.