Content area
Background
Although artificial intelligence (AI) has gained increasing attention for its potential future impact on clinical practice, medical education has struggled to stay ahead of the developing technology. The question of whether medical education is fully preparing trainees to adapt to potential changes from AI technology in clinical practice remains unanswered, and the influence of AI on medical students’ career preferences remains unclear. Understanding the gap between students’ interest in and knowledge of AI may help inform the medical curriculum structure.
Methods
A total of 354 medical students were surveyed to investigate their knowledge of, exposure to, and interest in the role of AI in health care. Students were questioned about the anticipated impact of AI on medical specialties and their career preferences.
Results
Most students (65%) were interested in the role of AI in medicine, but only 23% had received formal education in AI based on reliable scientific resources. Despite their interest and willingness to learn, only 20.1% of students reported that their school offered resources enabling them to explore the use of AI in medicine. They relied mainly on informal information sources, including social media, and few students understood fundamental AI concepts or could cite clinically relevant AI research. Students who cited more scientific primary sources (rather than online media) exhibited significantly higher self-reported understanding of AI concepts in the context of medicine. Interestingly, students who had received more exposure to AI courses reported higher levels of skepticism regarding AI and were less eager to learn more about it. Radiology and pathology were perceived to be the fields most strongly affected by AI. Students reported that their overall choice of specialty was not impacted by AI.
Conclusion
Formal AI education seems inadequate despite students’ enthusiasm concerning the application of such technology in clinical practice. Medical curricula should evolve to promote structured, evidence-based AI literacy to enable students to understand the potential applications of AI in health care.
Details
Physicians;
Grade Point Average;
Data Collection;
Influence of Technology;
Lecture Method;
Likert Scales;
Medical Education;
Educational Change;
Medical Evaluation;
Medical Students;
Student Interests;
Student Surveys;
Trainees;
Artificial Intelligence;
Student Participation;
Statistical Analysis;
Information Sources;
Statistical Data;
Data Processing;
Learner Engagement;
College Science;
Regression (Statistics);
Resource Allocation;
Algorithms