Previously submitted to: JMIR Medical Education (no longer under consideration since Jun 24, 2026)
Date Submitted: Sep 8, 2025
Open Peer Review Period: Sep 15, 2025 - Nov 10, 2025
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Comparison of ChatGPT and Online Medical Databases for Information Retrieval Among Medical Students: Quasi-Experimental Study
ABSTRACT
Background:
Medical students increasingly rely on online tools for clinical information retrieval. While traditional platforms like Google, PubMed, and UpToDate are commonly used, the rise of large language models (LLMs) such as ChatGPT offers new potential in this area.
Objective:
This study compared ChatGPT with conventional online resources in terms of accuracy, speed, and user satisfaction when retrieving medical information among sixth-year medical students.
Methods:
We conducted a quasi-experimental, posttest-only study with 64 sixth-year medical students at a Thai teaching hospital. Students were initially randomly assigned to use either ChatGPT or traditional online medical resources (Google, PubMed, and UpToDate), with final groups of 35 and 29 participants respectively due to protocol deviations. Participants completed a 10-question open-ended test on internal medicine topics. Responses were scored using expert-validated rubrics with multiple key elements per question, totaling 10 points each. We also recorded completion time and assessed satisfaction via a 15-item Likert questionnaire. Between-group comparisons used independent t-tests.
Results:
The ChatGPT group achieved significantly higher accuracy scores (mean 63.79, SD 9.71) than the control group (mean 47.22, SD 8.82, P<.001), reflecting a 35.1% improvement. They also completed the task more quickly (mean 58.59 vs 75.76 minutes, P=.003), with a 22.7% time reduction. Satisfaction scores were higher overall in the ChatGPT group (mean 62.06, SD 7.14 vs 56.72, SD 10.23; P=.018), particularly in the search process domain (P<.001). Performance varied by cognitive level, with application-type questions showing the largest advantage (20.63 percentage points), followed by remembering-level questions (16.14 percentage points) and analysis-level questions (8.54 percentage points). No significant associations were found between ChatGPT use and demographics, prior experience, or AI attitudes.
Conclusions:
ChatGPT significantly outperformed traditional online resources in accuracy, speed, and user satisfaction for clinical information retrieval among medical students. These findings suggest its potential as a supportive tool in medical education. However, future integration should include training in critical appraisal and responsible use to ensure safe and effective application in learning environments.
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