Medical Student Experiences and Perceptions of ChatGPT and Artificial Intelligence: Cross-Sectional Study.

AI ChatGPT LLMs artificial intelligence cross-sectional study education health care professionals large language models medical education medical student medical students medicine risk technology training

Journal

JMIR medical education
ISSN: 2369-3762
Titre abrégé: JMIR Med Educ
Pays: Canada
ID NLM: 101684518

Informations de publication

Date de publication:
22 Dec 2023
Historique:
received: 27 07 2023
accepted: 11 12 2023
revised: 10 11 2023
medline: 22 12 2023
pubmed: 22 12 2023
entrez: 22 12 2023
Statut: epublish

Résumé

Artificial intelligence (AI) has the potential to revolutionize the way medicine is learned, taught, and practiced, and medical education must prepare learners for these inevitable changes. Academic medicine has, however, been slow to embrace recent AI advances. Since its launch in November 2022, ChatGPT has emerged as a fast and user-friendly large language model that can assist health care professionals, medical educators, students, trainees, and patients. While many studies focus on the technology's capabilities, potential, and risks, there is a gap in studying the perspective of end users. The aim of this study was to gauge the experiences and perspectives of graduating medical students on ChatGPT and AI in their training and future careers. A cross-sectional web-based survey of recently graduated medical students was conducted in an international academic medical center between May 5, 2023, and June 13, 2023. Descriptive statistics were used to tabulate variable frequencies. Of 325 applicants to the residency programs, 265 completed the survey (an 81.5% response rate). The vast majority of respondents denied using ChatGPT in medical school, with 20.4% (n=54) using it to help complete written assessments and only 9.4% using the technology in their clinical work (n=25). More students planned to use it during residency, primarily for exploring new medical topics and research (n=168, 63.4%) and exam preparation (n=151, 57%). Male students were significantly more likely to believe that AI will improve diagnostic accuracy (n=47, 51.7% vs n=69, 39.7%; P=.001), reduce medical error (n=53, 58.2% vs n=71, 40.8%; P=.002), and improve patient care (n=60, 65.9% vs n=95, 54.6%; P=.007). Previous experience with AI was significantly associated with positive AI perception in terms of improving patient care, decreasing medical errors and misdiagnoses, and increasing the accuracy of diagnoses (P=.001, P<.001, P=.008, respectively). The surveyed medical students had minimal formal and informal experience with AI tools and limited perceptions of the potential uses of AI in health care but had overall positive views of ChatGPT and AI and were optimistic about the future of AI in medical education and health care. Structured curricula and formal policies and guidelines are needed to adequately prepare medical learners for the forthcoming integration of AI in medicine.

Sections du résumé

BACKGROUND BACKGROUND
Artificial intelligence (AI) has the potential to revolutionize the way medicine is learned, taught, and practiced, and medical education must prepare learners for these inevitable changes. Academic medicine has, however, been slow to embrace recent AI advances. Since its launch in November 2022, ChatGPT has emerged as a fast and user-friendly large language model that can assist health care professionals, medical educators, students, trainees, and patients. While many studies focus on the technology's capabilities, potential, and risks, there is a gap in studying the perspective of end users.
OBJECTIVE OBJECTIVE
The aim of this study was to gauge the experiences and perspectives of graduating medical students on ChatGPT and AI in their training and future careers.
METHODS METHODS
A cross-sectional web-based survey of recently graduated medical students was conducted in an international academic medical center between May 5, 2023, and June 13, 2023. Descriptive statistics were used to tabulate variable frequencies.
RESULTS RESULTS
Of 325 applicants to the residency programs, 265 completed the survey (an 81.5% response rate). The vast majority of respondents denied using ChatGPT in medical school, with 20.4% (n=54) using it to help complete written assessments and only 9.4% using the technology in their clinical work (n=25). More students planned to use it during residency, primarily for exploring new medical topics and research (n=168, 63.4%) and exam preparation (n=151, 57%). Male students were significantly more likely to believe that AI will improve diagnostic accuracy (n=47, 51.7% vs n=69, 39.7%; P=.001), reduce medical error (n=53, 58.2% vs n=71, 40.8%; P=.002), and improve patient care (n=60, 65.9% vs n=95, 54.6%; P=.007). Previous experience with AI was significantly associated with positive AI perception in terms of improving patient care, decreasing medical errors and misdiagnoses, and increasing the accuracy of diagnoses (P=.001, P<.001, P=.008, respectively).
CONCLUSIONS CONCLUSIONS
The surveyed medical students had minimal formal and informal experience with AI tools and limited perceptions of the potential uses of AI in health care but had overall positive views of ChatGPT and AI and were optimistic about the future of AI in medical education and health care. Structured curricula and formal policies and guidelines are needed to adequately prepare medical learners for the forthcoming integration of AI in medicine.

Identifiants

pubmed: 38133911
pii: v9i1e51302
doi: 10.2196/51302
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e51302

Informations de copyright

©Saif M I Alkhaaldi, Carl H Kassab, Zakia Dimassi, Leen Oyoun Alsoud, Maha Al Fahim, Cynthia Al Hageh, Halah Ibrahim. Originally published in JMIR Medical Education (https://mededu.jmir.org), 22.12.2023.

Auteurs

Saif M I Alkhaaldi (SMI)

Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.

Carl H Kassab (CH)

Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.

Zakia Dimassi (Z)

Department of Medical Science, Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.

Leen Oyoun Alsoud (L)

Department of Medical Science, Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.

Maha Al Fahim (M)

Education Institute, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates.

Cynthia Al Hageh (C)

Department of Medical Science, Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.

Halah Ibrahim (H)

Department of Medical Science, Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.

Classifications MeSH