Introduction In the recent years Artificial Intelligence has grown in popularity also in the field of Plastic Surgery; For patients is becoming more and more common to use internet to gather plastic surgery information and chatbot based on AI, such as ChatGPT, could be used for answering patient’s questions. The aim of this study was to assess the quality of medical information provided by ChatGPT regarding three of the most common procedure performed in breast surgery: Breast Reconstruction, Breast reduction and Augmentation mammaplasty. Methods The quality of information was evaluated through the Expanded EQIP scale. Responses where collected from a pool made by ten resident doctors in Plastic Surgery and then processed by SPSS software vers. 28.0. Results The analysis of the contents provided from ChatGPT showed a sufficient quality of the information in all the topic selected, with an high bias in terms of distribution of the score between the different items. There was a critical lack in the “Information data field” (0/6 score in all the 3 investigations) but a very high overall evaluation concerning the “Structure data” (>7/11 in all the 3 investigations). Conclusion At the present time, AI could be an useful tool for patients, but some critical issues that need to be faced from engineers and developers are still present. It’s definitely possible that in the future models like ChatGPT will play an important role in improving patient’s consciousness about medical procedures and surgical interventions, but their role must be considered ancillary to the one of the surgeon.

Quality of the Information provided by ChatGPT for Patients in Breast Plastic Surgery: Are we already in the future? / Grippaudo, F. R.; Nigrelli, S.; Patrignani, A; Ribuffo, D. - In: JPRAS OPEN. - ISSN 2352-5878. - 40:(2024), pp. 99-105. [10.1016/j.jpra.2024.02.001]

Quality of the Information provided by ChatGPT for Patients in Breast Plastic Surgery: Are we already in the future?

F. R. Grippaudo
Conceptualization
;
S. Nigrelli
Secondo
Writing – Original Draft Preparation
;
A Patrignani
Penultimo
Investigation
;
D Ribuffo
Ultimo
Supervision
2024

Abstract

Introduction In the recent years Artificial Intelligence has grown in popularity also in the field of Plastic Surgery; For patients is becoming more and more common to use internet to gather plastic surgery information and chatbot based on AI, such as ChatGPT, could be used for answering patient’s questions. The aim of this study was to assess the quality of medical information provided by ChatGPT regarding three of the most common procedure performed in breast surgery: Breast Reconstruction, Breast reduction and Augmentation mammaplasty. Methods The quality of information was evaluated through the Expanded EQIP scale. Responses where collected from a pool made by ten resident doctors in Plastic Surgery and then processed by SPSS software vers. 28.0. Results The analysis of the contents provided from ChatGPT showed a sufficient quality of the information in all the topic selected, with an high bias in terms of distribution of the score between the different items. There was a critical lack in the “Information data field” (0/6 score in all the 3 investigations) but a very high overall evaluation concerning the “Structure data” (>7/11 in all the 3 investigations). Conclusion At the present time, AI could be an useful tool for patients, but some critical issues that need to be faced from engineers and developers are still present. It’s definitely possible that in the future models like ChatGPT will play an important role in improving patient’s consciousness about medical procedures and surgical interventions, but their role must be considered ancillary to the one of the surgeon.
2024
Artificial Intelligence; ChatGPT; EQIP Scale; Augmentation mammaplasty; Breast reduction; Breast reconstruction
01 Pubblicazione su rivista::01a Articolo in rivista
Quality of the Information provided by ChatGPT for Patients in Breast Plastic Surgery: Are we already in the future? / Grippaudo, F. R.; Nigrelli, S.; Patrignani, A; Ribuffo, D. - In: JPRAS OPEN. - ISSN 2352-5878. - 40:(2024), pp. 99-105. [10.1016/j.jpra.2024.02.001]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1705169
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