ArticleFrontiers in artificial intelligence2025
Online platform vs. doctors: a comparative exploration of congenital cataract patient education from virtual to reality.
Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Comparative performance of contemporary multimodal large language models in retinal imaging question answering.Frontiers in medicine · 2026Article
- Large language models for ophthalmic examination understanding: from information extraction to clinical decision support.Frontiers in medicine · 2026Review
- Large Language Model-Generated Patient Education Materials in Pediatric Ophthalmology: A Scoping Review.Clinical ophthalmology (Auckland, N.Z.) · 2026Review
- 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
Objective: This study aimed to assess the quality and readability of patient education on congenital cataracts provided by Google, ChatGPT, and clinical doctors. Given the rarity of congenital cataracts and the need for accessible, accurate information for parents, we sought to evaluate the platforms' effectiveness in delivering relevant health information. Methods and analysis: We developed two question banks related to congenital cataracts from different sources. Responses from Google, ChatGPT, and two doctors were evaluated across five criteria: correctness, completeness, readability, helpfulness, and safety. An ophthalmologist panel used a five-point Likert scale to score these responses. The readability of responses was also assessed using passage and readability statistics, with additional readability enhancements applied to ChatGPT responses. Results: The ChatGPT responses demonstrated similar quality to those from experienced doctors, particularly excelling in readability, which was enhanced further with simplification techniques. Resident doctors provided the most readable doctor responses, while Google results scored the lowest across all five evaluative criteria. Post-enhancement, ChatGPT responses showed significant improvements in readability and maintained response quality. Conclusion: ChatGPT is a promising tool for delivering accessible, accurate information on congenital cataracts, especially for populations with lower health literacy. This study underscores the value of AI in healthcare education for rare conditions and highlights the need for consulting multiple information sources for comprehensive health guidance. ChatGPT, with readability enhancements, stands out as a particularly effective resource for public health information on congenital cataracts.
Indexed as
Identifiers
What OpenQuestion holds
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.