Generative AI's Impact on the Mental Health of Medical Students: Scenario Analysis

Clinical Snapshot

Key Findings

• Effect Size: Not applicable - qualitative scenario analysis

• Primary Outcome: Four distinct scenarios examining AI integration and mental health support interactions

• Nnt Or Sensitivity: Framework identifies systemic risks and institutional measures across four scenarios

Clinical Application

High feasibility for implementation in medical education institutions with appropriate planning and stakeholder engagement Highly relevant to Australian medical schools adapting to AI integration while maintaining student wellbeing standards aligned with RACGP and medical education accreditation requirements Medical educators, curriculum designers, medical students, and healthcare education policymakers

Abstract

BACKGROUND: Generative artificial intelligence (AI) is quickly changing medical education, even as medical students still face high levels of stress, anxiety, and burnout. These simultaneous trends-technological upheaval and ongoing mental health issues-bring up important questions about how future doctors will be trained and supported. Understanding how these factors might influence each other is crucial for developing resilient, future-ready medical education systems. OBJECTIVE: We carried out a foresight study using scenario analysis to examine potential futures at the crossroads of generative AI adoption and medical students' mental health. An initial environmental scan of the literature was conducted to pinpoint emerging trends and weak signals related to AI in medical education and well-being. These phenomena were categorized within a macro-meso-micro framework and analyzed through a multilevel sociotechnical change perspective. The study focused on 2 principal factors: the extent of generative AI integration into medical curricula and the availability of mental health support, as key drivers and critical uncertainties influencing future trajectories. METHODS: These dimensions resulted in 4 distinct scenarios: Analog Happiness (high support and low AI integration), Gen AI Paradise (high support and high integration), Disconnected Struggles (low support and low integration), and Gen AI Takeover (low support and high integration). Each scenario demonstrates how various institutional responses can impact students' digital readiness, psychological well-being, and professional growth. For each one, we identified the main systemic risks and suggested immediate institutional measures to address them. RESULTS: The findings suggest that technological innovation and mental health support must coevolve in medical education. Prioritizing one without the other risks producing either digitally unprepared or emotionally fragile physicians. Faculty readiness, ethical frameworks, and participatory curriculum design are critical to ensuring balanced integration. We formulated practical recommendations tailored to students, educators, and other stakeholders to guide balanced adaptation. CONCLUSIONS: Generative AI is more than just an additional tool in medical education; it is a systemic force that redefines how future physicians learn and operate. If technological change and student mental health are tackled separately, medical education risks creating graduates who are either unprepared for digital demands or mentally overwhelmed. This study highlights key systemic risks and suggests initial institutional steps to address them, providing a foresight-driven framework to assist educators and policymakers in responsible AI integration while safeguarding the well-being of future doctors.

References

1. 1.Arvai, N., Meskó, B., & Katonai, G. (2026). Generative AI's impact on the mental health of medical students: Scenario analysis. JMIR Medical Education, 12(1), e85373. https://doi.org/10.2196/85373