Learning to Think with AI: A Survey on Health Professional Students’ Use of Generative AI During Clinical Placements (Preprint)

BACKGROUND Generative artificial intelligence (GenAI) has rapidly expanded in higher education and clinical practice. Large language models such as ChatGPT are widely adopted by health profession students for learning and writing tasks. However, little is known about how these tools are mobilized during clinical placements, a critical stage of training where students face high cognitive demands and increasing responsibility for patient care. OBJECTIVE This study aimed to map self-reported uses of GenAI during clinical placements, assess perceived benefits and risks, and identify training and governance needs. METHODS We conducted a cross-sectional online survey at Université Grenoble Alpes (France) from June to September 2025. Eligible participants were students in medicine, pharmacy, nursing, midwifery, or physiotherapy who were currently in, or had completed within the past 18 months, a clinical placement. The 61-item questionnaire included closed and open-ended questions. A composite maturity score classified respondents as Minimal, Limited, Moderate, or High. Descriptive statistics and trend tests were used for analysis. RESULTS Among 388 respondents (79% female; 56% nursing, 18% medicine, 17% pharmacy, 6% midwifery, 3% physiotherapy), 53% reported using GenAI during placements. Uptake was lowest in midwifery (26%) and rose markedly with maturity (9% Minimal vs 76% High; P<.001). Students mainly used GenAI for information retrieval (78%), bibliographic search (75%), and translation/rephrasing (71%). Clinical-facing tasks such as case simulation (55%), drafting patient documents (38%), or preparing patient communication (38%) were less frequent, with fewer than 15% reporting weekly use. Most students avoided entering patient identifiers, but 23% acknowledged at least one disclosure, and 47% reported sharing anonymized medical data. Benefits were most often perceived for documentation support (81%) and information access (69%). Risks included dependence (91%), erosion of skills (85%), and confidentiality breaches (87%). Students highlighted strong needs for ethics/regulation training (78%), best-practice guidance (78%), profession-specific coaching (74%), and human–AI collaboration (73%). CONCLUSIONS GenAI is already embedded in the daily practices of health profession students during placements, primarily as a tool for documentation and information management. While students recognize its utility, they also express concerns about dependence, skills, and confidentiality. These findings underscore the urgent need for structured curricula and governance frameworks to support responsible and patient-centered integration of GenAI into clinical education.