Anxiety among Medical Students Regarding Generative Artificial Intelligence Models: A Pilot Descriptive Study

Abstract

Despite the potential benefits of generative artificial intelligence (genAI), concerns about its psychological impact on medical students, especially about job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed to examine the specific fears, anxieties, mistrust, and ethical concerns medical students harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 medical students studying in Jordan across various academic years, employing a structured self-administered questionnaire with an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, and Ethics—comprising 12 items, with 3 items for each construct. Exploratory and confirmatory factors analyses were conducted to assess the construct validity of the FAME scale. The results indicated variable levels of anxiety towards genAI among the participating medical students: 34.1% reported no anxiety about genAI‘s role in their future careers (n = 56), while 41.5% were slightly anxious (n = 61), 22.0% were somewhat anxious (n = 36), and 2.4% were extremely anxious (n = 4). Among the FAME constructs, Mistrust was the most agreed upon (mean: 12.35 ± 2.78), followed by the Ethics construct (mean: 10.86 ± 2.90), Fear (mean: 9.49 ± 3.53), and Anxiety (mean: 8.91 ± 3.68). Their sex, academic level, and Grade Point Average (GPA) did not significantly affect the students’ perceptions of genAI. However, there was a notable direct association between the students’ general anxiety about genAI and elevated scores on the Fear, Anxiety, and Ethics constructs of the FAME scale. Prior exposure to genAI and its previous use did not significantly modify the scores on the FAME scale. These findings highlight the critical need for refined educational strategies to address the integration of genAI into medical training. The results demonstrate notable anxiety, fear, mistrust, and ethical concerns among medical students regarding the deployment of genAI in healthcare, indicating the necessity of curriculum modifications that focus specifically on these areas. Interventions should be tailored to increase familiarity and competency with genAI, which would alleviate apprehensions and equip future physicians to engage with this inevitable technology effectively. This study also highlights the importance of incorporating ethical discussions into medical courses to address mistrust and concerns about the human-centered aspects of genAI. In conclusion, this study calls for the proactive evolution of medical education to prepare students for new AI-driven healthcare practices to ensure that physicians are well prepared, confident, and ethically informed in their professional interactions with genAI technologies.

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