JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is transforming medicine by enhancing care and reducing administrative tasks, and facilitating research. AI also raises many concerns, including a lack of clinical context awareness, data dependence, and the absence of ethical judgment. As future practitioners, medical students must be prepared for these changes. Most studies assessing students' attitudes and knowledge were conducted before artificial intelligence became accessible and tailored to the needs of the population. Therefore, how medical students actually use AI remains largely unexplored.

Objective:

This study explores French medical students' knowledge and attitudes toward AI.

Methods:

A mixed-methods study was conducted in 2025 among French medical students in their 4th to 6th year of school, corresponding to the clerkship year. An online survey adapted from Ten et al. 2025 included open-ended questions about AI definition and feelings toward AI, a Likert scale item to assess specific attitudes, and multiple-choice questions about the characteristics of the student. Quantitative analysis was performed using non-parametric tests (Kruskal-Wallis) to compare attitudes by AI knowledge level, academic years, career aspirations, and ranking within the class. Qualitative analysis was performed inductively.

Results:

Of 1,377 responses received, 1,342 were included. Students had a mean age of 23.1 years and were predominantly in their 5th year. Only 6% provided a correct definition of AI, while 51% gave incorrect responses. Attitudes toward AI were generally positive, with a mean score of 6.85, with significant differences by correct response to the definition (p <0.01; Unknown: 6.12, Incorrect: 6.84, Partially correct: 6.94, Correct: 6.88) and by career goals (p<0.01; clinical: 6.58; research: 6.83; private practice: 7.19). Regarding learning, 49% of students think that AI learning should be outside the curriculum, compared to 44%. Most of the students suggested AI training through multiple workshops Qualitative analysis revealed five themes: Representation, Nuanced Optimism, Critical Consideration, Replacement, and AI Use. Students represent AI as a robot, as an improved search engine, or as an unlimited data source. Their nuanced optimism blends enthusiasm for efficient patient care and provides an opportunity to focus more on the patient relationship, with fears of dehumanization, energy costs, and skill regression. Critical consideration underscores distrust in ethical dilemmas and data security risks. Replacement concerns arise over shifting professional roles, though many believe human empathy remains irreplaceable. For AI use, students highlight administrative aid, personalized training, and clinical support.

Conclusions:

There is growing interest in AI among medical students, accompanied by new ecological concerns and fears of skill loss. Students seem to have learned to use AI on their own for learning. These results highlight the need to adapt training programs to include the responsible use of these technologies and how to use AI to its fullest potential.