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ChatGPT vs. neurologists: a cross-sectional study investigating preference, satisfaction ratings and perceived empathy in responses among people living with multiple sclerosis

Articolo
Data di Pubblicazione:
2024
Abstract:
Background ChatGPT is an open-source natural language processing software that replies to users' queries. We conducted a cross-sectional study to assess people living with Multiple Sclerosis' (PwMS) preferences, satisfaction, and empathy toward two alternate responses to four frequently-asked questions, one authored by a group of neurologists, the other by ChatGPT. Methods An online form was sent through digital communication platforms. PwMS were blind to the author of each response and were asked to express their preference for each alternate response to the four questions. The overall satisfaction was assessed using a Likert scale (1-5); the Consultation and Relational Empathy scale was employed to assess perceived empathy. Results We included 1133 PwMS (age, 45.26 +/- 11.50 years; females, 68.49%). ChatGPT's responses showed significantly higher empathy scores (Coeff = 1.38; 95% CI = 0.65, 2.11; p > z < 0.01), when compared with neurologists' responses. No association was found between ChatGPT' responses and mean satisfaction (Coeff = 0.03; 95% CI = - 0.01, 0.07; p = 0.157). College graduate, when compared with high school education responder, had significantly lower likelihood to prefer ChatGPT response (IRR = 0.87; 95% CI = 0.79, 0.95; p < 0.01). Conclusions ChatGPT-authored responses provided higher empathy than neurologists. Although AI holds potential, physicians should prepare to interact with increasingly digitized patients and guide them on responsible AI use. Future development should consider tailoring AIs' responses to individual characteristics. Within the progressive digitalization of the population, ChatGPT could emerge as a helpful support in healthcare management rather than an alternative.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
Artificial intelligence; Large language model; Machine learning; Multiple sclerosis
Elenco autori:
Maida, Elisabetta; Moccia, Marcello; Palladino, Raffaele; Borriello, Giovanna; Affinito, Giuseppina; Clerico, Marinella; Repice, Anna Maria; Di Sapio, Alessia; Iodice, Rosa; Spiezia, Antonio Luca; Sparaco, Maddalena; Miele, Giuseppina; Bile, Floriana; Scandurra, Cristiano; Ferraro, Diana; Stromillo, Maria Laura; Docimo, Renato; De Martino, Antonio; Mancinelli, Luca; Abbadessa, Gianmarco; Smolik, Krzysztof; Lorusso, Lorenzo; Leone, Maurizio; Leveraro, Elisa; Lauro, Francesca; Trojsi, Francesca; Streito, Lidia Mislin; Gabriele, Francesca; Marinelli, Fabiana; Ianniello, Antonio; De Santis, Federica; Foschi, Matteo; De Stefano, Nicola; Morra, Vincenzo Brescia; Bisecco, Alvino; Coghe, Giancarlo; Cocco, Eleonora; Romoli, Michele; Corea, Francesco; Leocani, Letizia; Frau, Jessica; Sacco, Simona; Inglese, Matilde; Carotenuto, Antonio; Lanzillo, Roberta; Padovani, Alessandro; Triassi, Maria; Bonavita, Simona; Lavorgna, Luigi
Autori di Ateneo:
PADOVANI ALESSANDRO
Link alla scheda completa:
https://iris.unibs.it/handle/11379/613659
Pubblicato in:
JOURNAL OF NEUROLOGY
Journal
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