Knowledge, Attitude and Practice Towards Artificial Intelligence in Pharmaceutical Sciences Among Pharmacy Students and Graduates: A Cross-Sectional Study

Abstract

Background: Artificial intelligence (AI) is increasingly used in drug discovery, clinical research, and pharmacy practice. Little is known about how prepared pharmacy trainees and graduates are to use it. Objective: To assess knowledge, attitude, and practice (KAP) regarding AI in pharmaceutical sciences among pharmacy students and graduates, and to compare KAP across demographic and academic subgroups. Methods: We ran a cross-sectional online survey with a 16-item KAP questionnaire (5 knowledge, 6 attitude, 5 practice items). B.Pharm, M.Pharm, and Pharm.D students completed it, as did graduates working as faculty or in the pharmaceutical industry. Associations were tested with the chi-square test. Odds ratios (OR) with 95% confidence intervals (CI) were calculated for two-group comparisons. Significance was set at p < 0.05. Results: Of 327 responses, 306 were analysed (241 students, 65 graduates). Overall, 76.8% had heard of AI in pharmaceutical sciences, and 69.0% agreed it can be used in drug discovery. Only 56.5% reported using AI for pharmaceutical research and development (R&D). Gender and degree level (B.Pharm vs M.Pharm) were not associated with any KAP item. Students were more likely than graduates to use AI for R&D (60.2% vs 43.1%; OR 2.00, 95% CI 1.15–3.48; p = 0.020). They were also more likely to believe AI is more accurate than human experts (37.3% vs 15.4%; OR 3.28, 1.59–6.75; p = 0.001) and that AI will replace pharmacists (22.8% vs 9.2%; OR 2.91, 1.19–7.10; p = 0.024). Among students, later academic years showed higher agreement than the first year on three points: that AI supports drug discovery, that it will be widely used in the future, and that it should be taught. Use of AI for academic purposes followed the same pattern (all p < 0.05). Conclusion: Awareness of AI and attitudes towards it were favourable but cautious, while applied use lagged. Academic stage and student/graduate status were the main correlates of KAP. Gender and degree were not. Pharmacy curricula need structured, hands-on AI training, and graduates need the same through continuing education.