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Comparative accuracy of ChatGPT-4, Microsoft Copilot and Google Gemini in the Italian entrance test for healthcare sciences degrees: a cross-sectional study.
Rossettini, Giacomo; Rodeghiero, Lia; Corradi, Federica; Cook, Chad; Pillastrini, Paolo; Turolla, Andrea; Castellini, Greta; Chiappinotto, Stefania; Gianola, Silvia; Palese, Alvisa.
Afiliação
  • Rossettini G; School of Physiotherapy, University of Verona, Verona, Italy. giacomo.rossettini@gmail.com.
  • Rodeghiero L; Department of Physiotherapy, Faculty of Sport Sciences, Universidad Europea de Madrid, Villaviciosa de Odón, 28670, Spain. giacomo.rossettini@gmail.com.
  • Corradi F; Department of Rehabilitation, Hospital of Merano (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University (PMU), Merano-Meran, Italy. lia.rodeghiero@sabes.it.
  • Cook C; School of Speech Therapy, University of Verona, Verona, Italy.
  • Pillastrini P; Department of Orthopaedics, Duke University, Durham, NC, USA.
  • Turolla A; Duke Clinical Research Institute, Duke University, Durham, NC, USA.
  • Castellini G; Department of Population Health Sciences, Duke University, Durham, NC, USA.
  • Chiappinotto S; Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater University of Bologna, Bologna, Italy.
  • Gianola S; Unit of Occupational Medicine, IRCCS Azienda Ospedaliero-Universitaria Di Bologna, Bologna, Italy.
  • Palese A; Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater University of Bologna, Bologna, Italy.
BMC Med Educ ; 24(1): 694, 2024 Jun 26.
Article em En | MEDLINE | ID: mdl-38926809
ABSTRACT

BACKGROUND:

Artificial intelligence (AI) chatbots are emerging educational tools for students in healthcare science. However, assessing their accuracy is essential prior to adoption in educational settings. This study aimed to assess the accuracy of predicting the correct answers from three AI chatbots (ChatGPT-4, Microsoft Copilot and Google Gemini) in the Italian entrance standardized examination test of healthcare science degrees (CINECA test). Secondarily, we assessed the narrative coherence of the AI chatbots' responses (i.e., text output) based on three qualitative metrics the logical rationale behind the chosen answer, the presence of information internal to the question, and presence of information external to the question.

METHODS:

An observational cross-sectional design was performed in September of 2023. Accuracy of the three chatbots was evaluated for the CINECA test, where questions were formatted using a multiple-choice structure with a single best answer. The outcome is binary (correct or incorrect). Chi-squared test and a post hoc analysis with Bonferroni correction assessed differences among chatbots performance in accuracy. A p-value of < 0.05 was considered statistically significant. A sensitivity analysis was performed, excluding answers that were not applicable (e.g., images). Narrative coherence was analyzed by absolute and relative frequencies of correct answers and errors.

RESULTS:

Overall, of the 820 CINECA multiple-choice questions inputted into all chatbots, 20 questions were not imported in ChatGPT-4 (n = 808) and Google Gemini (n = 808) due to technical limitations. We found statistically significant differences in the ChatGPT-4 vs Google Gemini and Microsoft Copilot vs Google Gemini comparisons (p-value < 0.001). The narrative coherence of AI chatbots revealed "Logical reasoning" as the prevalent correct answer (n = 622, 81.5%) and "Logical error" as the prevalent incorrect answer (n = 40, 88.9%).

CONCLUSIONS:

Our main findings reveal that (A) AI chatbots performed well; (B) ChatGPT-4 and Microsoft Copilot performed better than Google Gemini; and (C) their narrative coherence is primarily logical. Although AI chatbots showed promising accuracy in predicting the correct answer in the Italian entrance university standardized examination test, we encourage candidates to cautiously incorporate this new technology to supplement their learning rather than a primary resource. TRIAL REGISTRATION Not required.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Avaliação Educacional Limite: Female / Humans / Male País/Região como assunto: Europa Idioma: En Revista: BMC Med Educ Assunto da revista: EDUCACAO Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Itália País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Avaliação Educacional Limite: Female / Humans / Male País/Região como assunto: Europa Idioma: En Revista: BMC Med Educ Assunto da revista: EDUCACAO Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Itália País de publicação: Reino Unido