ArticleJournal of multidisciplinary healthcare2024
Comparison of the Performance of ChatGPT, Claude and Bard in Support of Myopia Prevention and Control.
Article in Journal of multidisciplinary healthcare, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Assessing ChatGPT's Performance in Delineating Uveitis: An analysis of responses to real-world case presentations.medRxiv : the preprint server for health sciences · 2026Article
- Performance of GPT-5, DeepSeek, and Claude in dental MCQs for medically compromised patients.Journal of translational medicine · 2026Article
- Assessing large language model responses to pediatric depression FAQs: a cross-sectional study on readability, accuracy, and sentiment.Frontiers in psychiatry · 2026Article
- Large language models as information providers for appropriate antimicrobial use: computational text analysis and expert-rated comparison of ChatGPT, Claude and Gemini.BMJ health & care informatics · 2025Article
- Large language models in the management of chronic ocular diseases: a scoping review.Frontiers in cell and developmental biology · 2025Review
- Assessing large language models as assistive tools in medical consultations for Kawasaki disease.Frontiers in artificial intelligence · 2025Article
- Generative AI and large language models in nuclear medicine: current status and future prospects.Annals of nuclear medicine · 2024Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Chatbots, which are based on large language models, are increasingly being used in public health. However, the effectiveness of chatbot responses has been debated, and their performance in myopia prevention and control has not been fully explored. This study aimed to evaluate the effectiveness of three well-known chatbots-ChatGPT, Claude, and Bard-in responding to public health questions about myopia. Methods: Nineteen public health questions about myopia (including three topics of policy, basics and measures) were responded individually by three chatbots. After shuffling the order, each chatbot response was independently rated by 4 raters for comprehensiveness, accuracy and relevance. Results: The study's questions have undergone reliable testing. There was a significant difference among the word count responses of all 3 chatbots. From most to least, the order was ChatGPT, Bard, and Claude. All 3 chatbots had a composite score above 4 out of 5. ChatGPT scored the highest in all aspects of the assessment. However, all chatbots exhibit shortcomings, such as giving fabricated responses. Conclusion: Chatbots have shown great potential in public health, with ChatGPT being the best. The future use of chatbots as a public health tool will require rapid development of standards for their use and monitoring, as well as continued research, evaluation and improvement of chatbots.
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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.