Objectives Informed 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. Methods The 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. Results Consent 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. Conclusions The 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.

Potential role of ChatGPT in simplifying and improving informed consent forms for vaccination: a pilot study conducted in Italy / Cosma, Claudia; Radi, Alessio; Cattano, Rachele; Zanobini, Patrizio; Bonaccorsi, Guglielmo; Lorini, Chiara; Del Riccio, Marco. - In: BMJ HEALTH & CARE INFORMATICS. - ISSN 2632-1009. - ELETTRONICO. - 32:(2025), pp. e101248.1-e101248.7. [10.1136/bmjhci-2024-101248]

Potential role of ChatGPT in simplifying and improving informed consent forms for vaccination: a pilot study conducted in Italy

Cosma, Claudia;Radi, Alessio;Zanobini, Patrizio;Bonaccorsi, Guglielmo;Lorini, Chiara;Del Riccio, Marco
2025

Abstract

Objectives Informed 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. Methods The 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. Results Consent 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. Conclusions The 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.
2025
32
1
7
Goal 3: Good health and well-being
Cosma, Claudia; Radi, Alessio; Cattano, Rachele; Zanobini, Patrizio; Bonaccorsi, Guglielmo; Lorini, Chiara; Del Riccio, Marco
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1421114
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