Evidence map›Paper›PMID 41802232›Full record

ArticleJMIR formative research2026

Medical Student Experiences With ChatGPT: National Cross-Sectional Study.

Alan Yuesheng Xu, Skye Speakman, Vincent Salvatore Piranio, Robert Medina, Michelle Liu, Chris Lamprecht, Nicolas Abchee, Meghan Brennan

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Positive Affect and Academic Skill Development Through ChatGPT in Higher Education.European journal of investigation in health, psychology and education · 2026
    Article
  3. Article
  4. Article
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

8 authors.

Alan Yuesheng XuCollege of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0009-0000-7544-4923
Skye SpeakmanCollege of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0009-0007-0241-8848
Vincent Salvatore PiranioCollege of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0009-0007-0594-6400
Robert MedinaCollege of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0009-0004-1811-6105
Michelle LiuCollege of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0009-0000-2938-9900
Chris LamprechtCollege of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0009-0004-7578-0491
Nicolas AbcheeCollege of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0009-0007-7162-2711
Meghan BrennanCollege of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0009-0006-2598-5485

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is increasingly influencing medical student education, with AI-driven chatbots, such as ChatGPT, emerging as powerful study tools. While these technologies offer numerous benefits, they also pose challenges that warrant the adaptation of medical school curricula.

objectiveThis study examines medical students' perceptions and use of ChatGPT. We hypothesize that ChatGPT is widely used for academic support, but concerns remain regarding reliability and academic integrity.

methodsWe conducted a cross-sectional study from August 25 to December 10, 2024, in the United States. Students in all years of medical training who were enrolled in accredited allopathic or osteopathic medical schools were eligible to participate. Data were collected using an anonymous online questionnaire, which was distributed through institutional mailing lists. Overall, 188 schools were reached, of which 14 (7.4%) responded and agreed to distribute the survey. A total of 177 participants completed the survey. Survey items consisted primarily of Likert-scale and multiple-choice questions. Primary outcome measures included self-reported frequency of ChatGPT use, perceived usefulness of ChatGPT, and ChatGPT use habits.

resultsOverall, 98.9% (175/177) of participants had heard of ChatGPT, with 88.7% (157/177) reporting having used it; 62.7% (111/177) identified as female, and 52% (92/177) had completed at least 1 block of clinical rotations. Medical students most often used ChatGPT to understand complex medical concepts, prepare for exams, and generate study materials. Moreover, 46.5% (73/157) used it to help complete medical school assignments. Medical students also reported using it clinically, with the most common use being to generate differential diagnoses. Notably, 21.0% (33/157) of participants responded having used ChatGPT to help write clinical notes. Moreover, 73.9% (116/157) reported that their experience with ChatGPT improved their overall perception of AI's potential to assist in medical practice, and 86.6% (135/157) believed that having ChatGPT as a resource would make them more effective physicians. Statistical analyses were performed using the Pearson chi-square test with α=.05. Students who reported moderate or advanced baseline understanding of AI were more likely to practice conscientious use habits, such as cross-checking (odds ratio [OR] 2.31, 95% CI 1.08-4.97) and editing (OR 2.45, 95% CI 1.05-5.71) ChatGPT output before using it, than those who reported a basic or limited understanding.

conclusionsOur study is among the few to examine medical student perceptions of ChatGPT at a national level. We examined responsible use habits to identify areas in which reliance on this technology may lead users astray. We found that ChatGPT is being used to complete academic assignments and write clinical notes, raising concerns about information verification, AI literacy, patient confidentiality, and ethical use. Together, these findings highlight the need for structured AI education to help students leverage these technologies effectively while mitigating risks associated with misinformation and overreliance on AI.

Indexed as

Students, MedicalAdultCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansMaleSurveys and QuestionnairesUnited StatesAIartificial intelligenceChatGPTlarge language modelmedical educationmedical student

Identifiers

PMID41802232
PMCPMC13010067

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.