ArticlePloS one2025
Evaluating the quality of ChatGPT-generated medical information on major ophthalmic conditions: A comparative assessment against the EQIP tool and guidelines.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence (AI) and Healthcare Capabilities: A Systematic Review and Research Directions.F1000Research · 2025Pooled it
- Safety and quality of public chatbots for lung cancer prognostic information: a comparative evaluation.Frontiers in public health · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe use of artificial intelligence for creating medical information is on the rise. Nonetheless, the accuracy and reliability of such information require thorough assessment. As a language model capable of generating text, ChatGPT needs a detailed examination of its effectiveness in the healthcare domain.
objectiveThis research sought to evaluate the precision of medical data produced by ChatGPT-4o (https://chat.openai.com/chat, accessed Mar. 12, 2025), concentrating on its capability to handle the top five ophthalmic issues that pose the greatest global health challenges. Furthermore, the investigation compared the AI's answers to recognized medical guides.
methodsThis research employed an adapted version of the Ensuring Quality of Information for Patients (EQIP) instrument to evaluate the quality of ChatGPT's replies. The guidelines for the five conditions were rephrased into pertinent queries. These queries were then fed into ChatGPT, employing benchmarking against established ophthalmology clinical guidelines, and the resulting answers were independently scrutinized for precision and consistency by two investigators. The consistency among raters was evaluated using Cohen's kappa value.
resultsThe median EQIP score across the five conditions was 18 (IQR 18-19). The modified EQIP instrument revealed a robust consensus between the two evaluators when assessing ChatGPT's responses, as indicated by a Cohen's kappa value of 0.926 (95% CI 0.875-0.977, P<0.001). The alignment between the ChatGPT responses and the guideline recommendations was 84% (21/25), as indicated by a Cohen's kappa value of 0.658 (95% CI 0.317-0.999, P<0.001).
conclusionsChatGPT demonstrates robust quality and guideline compliance in producing medical content. Nevertheless, improvements are necessary to enhance the accuracy of quantitative data and ensure a more comprehensive coverage, thereby offering valuable insights for the advancement of medical information generation.
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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.