Evidence map›Paper›PMID 38133911›Full record

ArticleJMIR medical education2023

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

Saif M I Alkhaaldi, Carl H Kassab, Zakia Dimassi, Leen Oyoun Alsoud, Maha Al Fahim, Cynthia Al Hageh, Halah Ibrahim

Abstract read
In one paragraph

Article in JMIR medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

70 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. AI In the Chair: ChatGPT In Dental Education-A Cross-Sectional Study of Student Use and Perceptions in New Zealand.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

10 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Saif M I Alkhaaldi *Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.ORCID 0009-0007-9443-7420
Carl H Kassab *Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.ORCID 0009-0007-0902-4104
Zakia DimassiDepartment of Medical Science, Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.ORCID 0000-0001-8729-1581
Leen Oyoun AlsoudDepartment of Medical Science, Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.ORCID 0009-0007-9361-9759
Maha Al FahimEducation Institute, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates.ORCID 0000-0002-1595-6975
Cynthia Al HagehDepartment of Medical Science, Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.ORCID 0000-0001-8386-4063
Halah IbrahimDepartment of Medical Science, Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.ORCID 0000-0002-9240-7726

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial 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.

objectiveThe 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.

methodsA 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.

resultsOf 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).

conclusionsThe 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.

Indexed as

MedicineStudents, MedicalAcademic Medical CentersArtificial IntelligenceCross-Sectional StudiesHumansMaleAIartificial intelligenceChatGPTcross-sectional studyeducationhealth care professionalslarge language modelsLLMsmedical educationmedical studentmedical studentsmedicinerisktechnologytraining

Identifiers

PMID38133911
PMCPMC10770787

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.