ArticleBMJ health & care informatics2025
Potential role of ChatGPT in simplifying and improving informed consent forms for vaccination: a pilot study conducted in Italy.
Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline 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 for simplifying non-English medical consent with clinician involvement.NPJ digital medicine · 2026Article
- Mapping applications and evaluations of LLM-enabled AI chatbots for health purposes: a scoping review.Frontiers in public health · 2026Article
- LuminaConsent: AI-driven standardization and quality enhancement of urological informed consent documentation.Northern clinics of Istanbul · 2026Article
- Applications of Artificial Intelligence in the Control of Infectious Diseases in the Post-COVID Era: Scoping Review.JMIR nursing · 2025Article
- Better understanding: can a large language model safely improve readability of patient information leaflets in interventional radiology?BMJ health & care informatics · 2025Article
- The AI-educated patient.Digital healthArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesInformed consent forms are important for assisting patients in making informed choices regarding medical procedures. Because of their lengthy nature, complexity and specialised terminology, consent forms usually prove challenging for the general public to comprehend. This pilot study aims to use Chat Generative Pretrained Transformer (ChatGPT), a large language model (LLM), to improve the readability and understandability of a consent form for vaccination.
methodsThe study was conducted in Italy, within the Central Tuscany Local Health Unit. Three different consent forms were selected and approved: the standard consent form currently in use (A), a new form totally generated by ChatGPT (B) and a modified version of the standard form created by ChatGPT (C). Healthcare professionals in the vaccination unit were asked to evaluate the consent forms regarding adequacy, comprehensibility and completeness and to give an overall judgement. The Kruskal-Wallis test and Dunn's test were used to evaluate the median scores of the consent forms across these variables.
resultsConsent forms A and C achieved the top scores in every category; consent form B obtained the lowest score. The median scores were 4.0 for adequacy on consent forms A and C and 3.0 on consent form B. Consent forms A and C received high overall judgement ratings with median scores of 4.0, whereas consent form B received a median score of 3.0.
conclusionsThe findings indicate that LLM tools such as ChatGPT could enhance healthcare communication by improving the clarity and accessibility of consent forms, but the best results are seen when these tools are combined with human knowledge and supervision.
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