Understanding Generative Artificial Intelligence Adoption in Puerto Rican Medical Schools: A Cross-Institutional Survey of First- and Second-Year Students

Abstract

Background: Generative Artificial Intelligence (GenAI) has rapidly gained traction in medical education, yet little is known about its use among medical students in Puerto Rico. This study examines GenAI usage patterns among first- and second-year students across all four Liaison Committee on Medical Education-accredited medical schools in Puerto Rico, with comparisons based on user experience. Methods: A cross-institutional electronic survey was conducted from January to June 2024 among first- and second-year medical students (n = 194) from Ponce Health Sciences University, University of Puerto Rico Medical Sciences Campus, San Juan Bautista School of Medicine, and Universidad Central del Caribe. Students were grouped by program year, and GenAI experience (<1 year vs ≥ 1 year). Chi-square tests were used to evaluate group differences (P < 0.05). Results: Of the 778 first- and second-year medical students enrolled across all schools, 194 responded. Most (72.7%) reported using GenAI, primarily ChatGPT (89.9%), mainly for academic purposes (75.3%). Students with ≥1 year of GenAI experience were more likely to agree that GenAI helped them understand research papers (89.9%) compared to 51.6% of less-experienced users who disagreed (P < 0.001). From this same cohort, 72.7% also planned to use GenAI for board exam preparation, compared to 63.2% of less-experienced users who did not plan to use it. Although 72.2% believe GenAI will be integrated into Puerto Rico's healthcare system, only 52.1% felt that local medical facilities would be receptive. Challenges such as limited data access (27%) and power outages (34%) were more commonly reported by less-experienced users. Conclusion: Generative AI adoption among Puerto Rican medical students is expanding, mainly for academic purposes. Greater experience correlates with higher perceived benefit, emphasizing the need for structured, ethical GenAI training, and institutional support within medical education.

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