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Accepted for/Published in: JMIR Medical Education

Date Submitted: Aug 10, 2023
Open Peer Review Period: Mar 12, 2024 - May 7, 2024
Date Accepted: Apr 30, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Understanding Health Care Students’ Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study

Cherrez-Ojeda I, Robles-Velasco K, Osorio MF, Gallardo-Bastidas JC, Velez Leon EM, Leon Velastegui M, Pauletto P, Aguilar-Díaz F, Squassi A, González Eras SP, Cordero Carrasco E, Chavez Gonzalez KL, Calderon JC, Bousquet J, Bedbrook A, Faytong-Haro M

Understanding Health Care Students’ Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study

JMIR Med Educ 2024;10:e51757

DOI: 10.2196/51757

PMID: 39137029

PMCID: 11350293

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Understanding Healthcare Students' Perceptions, Beliefs, and Attitudes Toward AI-powered Language Models: Charting the Course for AI in Healthcare Education in the Americas.

  • Ivan Cherrez-Ojeda; 
  • Karla Robles-Velasco; 
  • María F. Osorio; 
  • Juan C. Gallardo-Bastidas; 
  • Eleonor Maria Velez Leon; 
  • Manuel Leon Velastegui; 
  • Patrícia Pauletto; 
  • FC Aguilar-Díaz; 
  • Aldo Squassi; 
  • Susana Patricia González Eras; 
  • Erita Cordero Carrasco; 
  • Karol Leonor Chavez Gonzalez; 
  • Juan C Calderon; 
  • Jean Bousquet; 
  • Anna Bedbrook; 
  • Marco Faytong-Haro

ABSTRACT

Background:

ChatGPT was not intended for use in healthcare, but it has potential benefits that depend on end-user understanding and acceptability, which is where healthcare students become crucial and there is still a limited amount of research.

Objective:

We aim to understand healthcare students' ChatGPT perceptions, ethical considerations, use, and attitudes.

Methods:

A cross-sectional survey of medical, nursing, dentistry, nutrition, and laboratory science students across the Americas was undertaken from May to June 2023. Descriptive analysis, Chi-square, and ANOVA for statistical significance across categories were used. Multiple linear regression models examined the effect of perception scores on attitude variables. All models were adjusted for gender, institution type, major, and country. STATA 18.0 performed all analyses.

Results:

Of 2661 healthcare students, 43% were unaware of ChatGPT. The average score of knowledge was "minimal” (mean 2.03±1.19). Most respondents regarded ChatGPT as neither ethical nor unethical. Most participants "somewhat agree" that ChatGPT (i) benefits healthcare settings, (ii) provides trustworthy data, (iii) is a helpful tool for clinical and educational medical information access, and (iv) makes the work easier. 7/10 people use it for homework. Higher knowledge and ethical scores raise the chance of considering ChatGPT as a trustworthy healthcare information source by up to 30 times. Higher ethical consideration perception ratings increase the likelihood of considering ChatGPT as positive (coefficient=0.265), beneficial for medical issues, and useful for medical literature (0.274 and 0.261, respectively). All results were significant.

Conclusions:

Over 40% of American healthcare students were unaware of ChatGPT, despite its extensive use in the health field. Our data reveal ChatGPT's positive attitudes and desire to learn more. Medical educators must explore how chatbots may be included in undergraduate healthcare education programs. Clinical Trial: N/A


 Citation

Please cite as:

Cherrez-Ojeda I, Robles-Velasco K, Osorio MF, Gallardo-Bastidas JC, Velez Leon EM, Leon Velastegui M, Pauletto P, Aguilar-Díaz F, Squassi A, González Eras SP, Cordero Carrasco E, Chavez Gonzalez KL, Calderon JC, Bousquet J, Bedbrook A, Faytong-Haro M

Understanding Health Care Students’ Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study

JMIR Med Educ 2024;10:e51757

DOI: 10.2196/51757

PMID: 39137029

PMCID: 11350293

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