Awareness and Attitudes Toward Artificial Intelligence in Healthcare Among Undergraduate Medical and Allied Health Students: A Cross-Sectional Study
Abstract
Background: Artificial intelligence (AI) is increasingly integrated into diagnostics, clinical decision-making, and medical education, yet its adoption among future healthcare providers depends on their awareness and attitudes toward the technology. Objective: To determine the level of awareness and assess the attitudes toward AI in healthcare among undergraduate medical and allied health students in Faisalabad, Pakistan, and to evaluate the association between awareness and attitudes. Methodology: A cross-sectional analytical study was conducted among 200 undergraduate students enrolled in MBBS, DPT, Nursing, Pharm-D, and Radiology/Medical Laboratory Technology programs at three institutions in Faisalabad, recruited through convenience sampling. A structured self-administered questionnaire adapted from Castagno and Khalifa (2020) was used. Data were analyzed in SPSS version 26 using descriptive statistics and chi-square tests, with significance set at p ≤ 0.05. Results: Most participants (65.0%) demonstrated moderate awareness of AI, while 20.0% had high awareness and 15.0% had low awareness. A majority (70.0%) held positive attitudes toward AI in healthcare; 81.0% agreed that AI improves healthcare quality and 85.0% considered it useful for future healthcare practice. Fewer participants agreed that AI may replace healthcare professionals (31.0%) or reduce empathy in patient care (27.0%). Awareness level was significantly associated with attitude toward AI (χ² = 24.82, df = 4, p < 0.001). Previous AI tool use was also significantly associated with positive attitudes (p = 0.011). Conclusion: Undergraduate healthcare students demonstrated moderate AI awareness and predominantly positive attitudes toward its role in healthcare. Higher awareness and previous AI tool use were associated with more positive attitudes, although the cross-sectional design precludes causal interpretation. Structured AI education incorporating clinical applications, ethical considerations, data security, bias, accountability, and AI limitations should be integrated into undergraduate healthcare curricula to strengthen responsible AI readiness.