Previously submitted to: JMIR Formative Research (no longer under consideration since Dec 02, 2025)
Date Submitted: Nov 19, 2024
Warning: This is an author submission that is not peer-reviewed or edited.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.
Auto-Generative Text-bots in Graduate Medical Education: Resident and Faculty Uses and Opinions
ABSTRACT
Background:
The rise of text-bots such as Chat Generative Pre-Trained Transformer prompts scrutiny of artificial intelligence (AI) in graduate medical education (GME), where data on provider and trainee views on AI's appropriateness and governance remain sparse.
Objective:
To identify common perceptions and concerns about text-bots among GME trainees and faculty and characterize differences in opinions.
Methods:
In 2023, we conducted a cross-sectional Qualtrics™-based survey of medical residents and faculty at Madigan Army Medical Center. Multiple choice and free response questions focused on text-bot uses and appropriateness of use in various settings. We completed descriptive analyses and obtained odds ratios (ORs) in Qualtrics™ and R. We compiled common themes from free responses.
Results:
43 trainees and 42 faculty responded to at least one question. Relative to faculty, trainees are 69% less likely to report text-bot use for evaluation preparation as appropriate (OR = 0.31, 95% CI: 0.10-0.88), and 2.5-times more likely to consider text-bot use for clinical note writing as appropriate (OR = 3.47, 95% CI: 1.18-10.86). Most trainees and residents agree text-bot utilization is appropriate for administrative tasks, education, learning, and research, with no differences in opinion between the two groups (p < .05).
Conclusions:
Trainees and faculty possess divergent opinions regarding the AI text-bot use for clinical notes and evaluation preparation. Concerns over privacy and oversight countered optimism surrounding efficiency and completion of tedious patient care tasks like letter generation. Additional studies should explore these views, and provider beliefs about medical application(s) of text-bots should inform GME AI policies.
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