ArticleBMJ open ophthalmology2024
Artificial intelligence chatbots as sources of patient education material for cataract surgery: ChatGPT-4 versus Google Bard.
Article in BMJ open ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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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
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Comparison of the readability of ChatGPT and Bard in medical communication: a meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Artificial intelligence in cataract management: current status and future perspectives.International ophthalmology · 2026Review
- Evaluating ChatGPT's Effectiveness for Arabic Dry Mouth Patient Education.Healthcare (Basel, Switzerland) · 2026Article
- Comparison of the performance of large language models in answering patient questions related to cataract.iScience · 2026Article
- Accuracy and Readability of Chat Generative Pre-Trained Transformer-4 Omni in Answering Ophthalmology Patient Questions.Ophthalmology science · 2026Article
- Revolutionizing patient care: assessing ai-created discharge education for total hip replacement patients.BMC medical education · 2025Article
- Evaluating the Effectiveness of Generative AI for the Creation of Patient Education Materials on Coronary Heart Disease: A Comparative Study.JMIR formative research · 2025Article
- Review
- 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
- Artificial Intelligence in Ophthalmology: Acceptance, Clinical Integration, and Educational Needs in Switzerland.Journal of clinical medicine · 2025Article
- Development and evaluation of an agentic LLM based RAG framework for evidence-based patient education.BMJ health & care informatics · 2025Article
- Evaluating Large Language Models in Ptosis-Related inquiries: A Cross-Lingual Study.Translational vision science & technology · 2025Article
- Evaluating the quality of large language model-generated preoperative patient education material: a comparative study across models and surgery types.Frontiers in medicine · 2025Article
- Comparison of artificial intelligence-generated and physician-generated patient education materials on early diabetic kidney disease.Frontiers in endocrinology · 2025Article
- Evaluating the reliability of large language models in answering FAQs for cataract surgery.Digital healthArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo conduct a head-to-head comparative analysis of cataract surgery patient education material generated by Chat Generative Pre-trained Transformer (ChatGPT-4) and Google Bard. METHODS AND ANALYSIS: 98 frequently asked questions on cataract surgery in English were taken in November 2023 from 5 trustworthy online patient information resources. 59 of these were curated (20 augmented for clarity and 39 duplicates excluded) and categorised into 3 domains: condition (n=15), preparation for surgery (n=21) and recovery after surgery (n=23). They were formulated into input prompts with 'prompt engineering'. Using the Patient Education Materials Assessment Tool-Printable (PEMAT-P) Auto-Scoring Form, four ophthalmologists independently graded ChatGPT-4 and Google Bard responses. The readability of responses was evaluated using a Flesch-Kincaid calculator. Responses were also subjectively examined for any inaccurate or harmful information.
resultsGoogle Bard had a higher mean overall Flesch-Kincaid Level (8.02) compared with ChatGPT-4 (5.75) (p<0.001), also noted across all three domains. ChatGPT-4 had a higher overall PEMAT-P understandability score (85.8%) in comparison to Google Bard (80.9%) (p<0.001), which was also noted in the 'preparation for cataract surgery' (85.2% vs 75.7%; p<0.001) and 'recovery after cataract surgery' (86.5% vs 82.3%; p=0.004) domains. There was no statistically significant difference in overall (42.5% vs 44.2%; p=0.344) or individual domain actionability scores (p>0.10). None of the generated material contained dangerous information.
conclusionIn comparison to Google Bard, ChatGPT-4 fared better overall, scoring higher on the PEMAT-P understandability scale and exhibiting more faithfulness to the prompt engineering instruction. Since input prompts might vary from real-world patient searches, follow-up studies with patient participation are required.
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