Artículo
Understanding Health Care Students’ Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study
Cherrez Ojeda, Iván; Gallardo Batidas, Juan C.; Robles Velasco, Karla; Osorio, María Valeria; Vélez León, Eleonor María; Leon Velastegui, Manuel; Pauletto, Patrícia; Aguilar Díaz, F. C.; Squassi, Aldo Fabian
; González Eras, Susana Patricia; Cordero Carrasco, Erita; Chavez Gonzalez, Karol Leonor; Calderon, Juan C.; Bousquet, Jean; Bedbrook, Anna; Faytong Haro, Marco
; González Eras, Susana Patricia; Cordero Carrasco, Erita; Chavez Gonzalez, Karol Leonor; Calderon, Juan C.; Bousquet, Jean; Bedbrook, Anna; Faytong Haro, Marco
Fecha de publicación:
08/2024
Editorial:
JMIR Publications
Revista:
JMIR Medical Education
ISSN:
2369-3762
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Background: ChatGPT was not intended for use in health care, but it has potential benefits that depend on end-user understandingand acceptability, which is where health care students become crucial. There is still a limited amount of research in this area.Objective: The primary aim of our study was to assess the frequency of ChatGPT use, the perceived level of knowledge, theperceived risks associated with its use, and the ethical issues, as well as attitudes toward the use of ChatGPT in the context ofeducation in the field of health. In addition, we aimed to examine whether there were differences across groups based ondemographic variables. The second part of the study aimed to assess the association between the frequency of use, the level ofperceived knowledge, the level of risk perception, and the level of perception of ethics as predictive factors for participants?attitudes toward the use of ChatGPT.Methods: A cross-sectional survey was conducted from May to June 2023 encompassing students of medicine, nursing, dentistry,nutrition, and laboratory science across the Americas. The study used descriptive analysis, chi-square tests, and ANOVA to assessstatistical significance across different categories. The study used several ordinal logistic regression models to analyze the impactof predictive factors (frequency of use, perception of knowledge, perception of risk, and ethics perception scores) on attitude asthe dependent variable. The models were adjusted for gender, institution type, major, and country. Stata was used to conduct allthe analyses.Results: Of 2661 health care students, 42.99% (n=1144) were unaware of ChatGPT. The median score of knowledge was?minimal? (median 2.00, IQR 1.00-3.00). Most respondents (median 2.61, IQR 2.11-3.11) regarded ChatGPT as neither ethicalnor unethical. Most participants (median 3.89, IQR 3.44-4.34) ?somewhat agreed? that ChatGPT (1) benefits health care settings,(2) provides trustworthy data, (3) is a helpful tool for clinical and educational medical information access, and (4) makes thework easier. In total, 70% (7/10) of people used it for homework. As the perceived knowledge of ChatGPT increased, there wasa stronger tendency with regard to having a favorable attitude toward ChatGPT. Higher ethical consideration perception ratingsincreased the likelihood of considering ChatGPT as a source of trustworthy health care information (odds ratio [OR] 1.620, 95%CI 1.498-1.752), beneficial in medical issues (OR 1.495, 95% CI 1.452-1.539), and useful for medical literature (OR 1.494, 95%CI 1.426-1.564; P<.001 for all results).Conclusions: Over 40% of American health care students (1144/2661, 42.99%) were unaware of ChatGPT despite its extensiveuse in the health field. Our data revealed the positive attitudes toward ChatGPT and the desire to learn more about it. Medicaleducators must explore how chatbots may be included in undergraduate health care education programs.
Palabras clave:
artificial intelligence
,
chatgpt
,
education
,
health care
Archivos asociados
Licencia
Identificadores
Colecciones
Articulos(OCA HOUSSAY)
Articulos de OFICINA DE COORDINACION ADMINISTRATIVA HOUSSAY
Articulos de OFICINA DE COORDINACION ADMINISTRATIVA HOUSSAY
Citación
Cherrez Ojeda, Iván; Gallardo Batidas, Juan C.; Robles Velasco, Karla; Osorio, María Valeria; Vélez León, Eleonor María; et al.; Understanding Health Care Students’ Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study; JMIR Publications; JMIR Medical Education; 10; 8-2024; 1-16
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