A Cross-Sectional Study to Assess the Knowledge, Attitude, and Practice Regarding Artificial Intelligence Among Undergraduate Medical Students at a Medical College

Abstract

Background: Artificial intelligence (AI) is increasingly being integrated into medical education and healthcare. Medical students are expected to develop adequate knowledge and responsible practices regarding AI, but gaps in formal training and critical appraisal of AI-generated information remain. This study assessed the knowledge, attitude, and practice (KAP) regarding AI among undergraduate medical students.

Methods: A cross-sectional descriptive study was conducted among 150 undergraduate medical students at a government medical college in Badaun. Data were collected using a structured, self-administered questionnaire covering sociodemographic characteristics, knowledge, attitude, and practice related to AI. Descriptive statistics were used to summarize the findings, and associations and correlations between KAP domains were assessed using appropriate statistical tests. A p-value <0.05 was considered statistically significant.

Results: Among the 150 students, 72.0% reported previous exposure to AI, whereas only 20.7% had received formal AI training. The mean knowledge, attitude, and practice scores were 14.7 ± 3.8, 35.7 ± 5.1, and 13.8 ± 3.7, respectively. Overall, 50.7% of students had moderate knowledge, 33.3% had good knowledge, and 16.0% had poor knowledge. Most students demonstrated good awareness of the need for human oversight (91.3%), the possibility of inaccurate AI-generated information (87.3%), and AI applications in medical imaging (85.3%). A favorable attitude towards AI was observed, with 76.7% agreeing or strongly agreeing that AI could improve medical education. AI tools were used by 79.3% of students, primarily for academic learning (60.7%). However, only 22.7% always verified AI-generated information. Significant positive correlations were observed between knowledge and attitude (r=0.38, p<0.001), knowledge and practice (r=0.46, p<0.001), and attitude and practice (r=0.41, p<0.001). Furthermore, 82.7% of students supported the inclusion of formal AI education in the undergraduate medical curriculum.

Conclusion: Undergraduate medical students demonstrated moderate knowledge and practice and a generally favorable attitude towards AI. Despite substantial exposure and use of AI tools, formal training was limited and consistent verification of AI-generated information was inadequate. Structured AI education focusing on practical applications, critical appraisal, ethics, privacy, algorithmic bias, and responsible use should be considered in undergraduate medical education.