JMIR Publications

ABSTRACT

Background:

Large language models (LLMs) such as ChatGPT offer significant opportunities for medical education. However, empirical data on actual usage patterns, perceived benefits, and limitations among medical students remain limited.

Objective:

This study aimed to assess how medical students in Germany use ChatGPT, their perceptions of its educational value, and the challenges and concerns associated with its use.

Methods:

A cross-sectional 17-item online survey was conducted between May and August 2024 among medical students from Philipps University Marburg, Germany. A mixed-methods approach was applied, combining descriptive and inferential statistical analysis with qualitative content analysis of open-ended responses.

Results:

A total of 84 fully completed surveys were included in the analysis (response rate: 26.7%). 76.2% (64/84) of the participants reported having used ChatGPT for medical education, with significantly higher usage during exam periods (p = .003). Preclinical students reported higher overall usage than clinical students (p = .020). ChatGPT was primarily used for summarizing information by 60.7% (51/84) of students, for literature research by 57.7% (49/84), and for clarifying concepts by 47.1% (40/84). A total of 70.2% (59/84) felt that it helped them save time, and 51.2% (43/84) reported an improved understanding of content. In contrast, only 31.0% (26/84) saw benefits for applying knowledge, and 15.5 % (13/84) for long-term knowledge retention. Free-text responses also revealed that students see further potential for ChatGPT in clinical documentation and exam preparation. However, 73.3% (55/75) expressed concerns about misinformation, and 72.6% (61/84) reported lacking confidence in their AI-related skills. Only 41.7% (35/84) stated that they trust ChatGPT’s outputs. Students who used the tool more frequently also reported higher levels of trust (r = 0.374, p < .001). Over 70% of respondents indicated a strong desire for increased integration of AI-related education and practical applications within the medical curriculum.

Conclusions:

ChatGPT is already widely used among medical students, especially in exam preparation and the early stages of training. Students value its efficiency and support for understanding complex material, but its long-term influence on learning is limited. Concerns about reliability, source transparency, and data privacy remain, and AI skills play a key role in shaping usage. These findings underscore the need to integrate structured, practice-oriented AI education into medical training to support critical, informed, and ethical use of LLMs.