Generative AI in Healthcare: Insights from Health Professions Educators and Students - Chaoyan Dong; Derrick Chen Wee Aw; Deanna Wai Ching Lee; Siew Ching Low; Clement C. Yan (International Medical Education, 2025)
The integration of Generative Artificial Intelligence (GenAI) into health professions education (HPE) is rapidly transforming learning environments, raising questions about its impact on teaching and learning. This mixed methods study explores clinical educators’ and undergraduate students’ perceptions and attitudes about using GenAI tools in HPE at a tertiary hospital in Singapore. Using the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) as theoretical frameworks, we designed and administered a survey and conducted interviews to assess participants’ perceived usefulness, ease of use, and concerns related to GenAI adoption. Quantitative survey data were analyzed for frequencies and percentages, while qualitative responses underwent thematic analysis. Results showed that students demonstrated higher GenAI adoption rates (68.7%) compared to educators (38.5%), with GenAI perceived as valuable for efficiency, research, and personalized learning. However, concerns included over-reliance on GenAI, diminished critical thinking, and ethical implications. Educators emphasized the need for institutional guidelines and training to support responsible GenAI integration. Our findings suggest that while GenAI holds great potential for enhancing education, structured institutional policies and ethical oversight are crucial for its effective use. These insights contribute to the ongoing discourse on GenAI adoption in HPE.