ArticleTranslational pediatrics2025
Evaluating ChatGPT's responses to vaccine-related questions: the impact of question framing on content and quality.
Article in Translational pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Assessment of vaccine information accuracy across large language models.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Vaccine hesitancy, fueled by mistrust, fear, and misinformation, remains a major public health challenge. Generative artificial intelligence tools such as ChatGPT have emerged as new information sources, particularly for younger users. It is essential that these tools provide medically accurate content, especially when responding to negatively framed vaccine questions. This study aims to examine how ChatGPT responds to vaccine-related questions, focusing on questions with negative or skeptical framing. Methods: This anonymous survey targeted board-certified pediatric infectious disease specialists in Japan to evaluate ChatGPT's responses to vaccine-related questions. Using ChatGPT-4o, 20 question pairs (supportive Results: Twenty of 22 invited specialists (90.9%) completed the survey. Median [25th-75th percentile] scores for supportive Conclusions: ChatGPT maintained comparable quality in Japanese responses to both supportive and critical vaccine questions, suggesting resilience to negative framing. However, expert reviewers identified thematic biases, occasional inadequacy of detail, and linguistic issues that could mislead lay readers. These findings underscore the need for continued human oversight, refinement of Japanese-language outputs, and algorithmic adjustments to reduce topical bias.
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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.