ArticleCureus2025
Assessing the Capabilities of Artificial Intelligence (AI) Tools in Community Medicine: A Comparative Study of ChatGPT, Gemini, and Bing in Community-Based Clinico-Social Case Interpretation.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- AI in respiratory care: findings from the GOLD report.Journal of translational medicine · 2026Article
- ChatGPT in Public Cardiovascular Healthcare: Accuracy, Limitations, and Implications.Journal of evaluation in clinical practice · 2026Article
- Evaluation of multimodal large language models for pneumothorax assessment in real-world clinical scenarios.BMC pulmonary medicine · 2026Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background and objective Artificial intelligence (AI) is being increasingly integrated into healthcare, offering opportunities to enhance decision-making, education, and patient engagement. Community medicine often involves interpreting clinico-social case studies that combine clinical, social, and environmental dimensions. However, there is limited research evaluating how AI tools perform in analyzing such community-based clinico-social cases. This study aimed to address that gap in the literature. Methods A comparative cross-sectional study was conducted using 30 standardized clinico-social case studies covering communicable and non-communicable diseases, maternal and child health, adolescent health, and social pathology. Three conversational AI models -ChatGPT (OpenAI, San Francisco, CA), Microsoft Bing AI (Microsoft Corp., Redmond, WA), Gemini (Google, Mountain View, CA) - were provided with identical prompts to interpret cases. Their responses were assessed by a panel of five community medicine experts using a 100-point rubric across five domains: diagnosis, intervention, recognition of social determinants, ethical reasoning, and public health appropriateness. Descriptive statistics and Spearman's rank correlation were used for analysis. Results All three AI tools demonstrated near-ceiling performance. Gemini achieved the highest total score (97.00 ± 1.74), excelling in diagnosis and public health appropriateness. ChatGPT (96.00 ± 2.98) performed best in intervention suggestions, while Bing AI (95.70 ± 3.64) showed slightly lower but comparable scores. Correlation analysis revealed weak-to-moderate alignment, with limited statistically significant associations across domains. Conclusions ChatGPT, Gemini, and Bing exhibit broadly similar capabilities in interpreting clinico-social cases, with domain-specific strengths. Their complementary nature suggests that they can aid medical education and public health practice, but should supplement rather than replace human expertise to minimize bias and errors.
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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.