Efficacy of a Stepped-Care Approach to Cognitive-Behavioural Therapy for Insomnia (Can-Sleep) in Adolescents and Young Adults With Cancer.

Artificial intelligence (AI) has emerged as a transformative technology in contemporary healthcare, influencing diagnostic processes, treatment planning, health system management, and professional education. Advances in machine learning, natural language processing, and generative AI have enabled automated clinical decision support, predictive analytics, and personalized patient care []. As these technologies continue to expand, healthcare professionals are increasingly expected to develop competencies in interpreting AI outputs, evaluating algorithmic recommendations, and applying digital tools responsibly within clinical practice. Consequently, health professions education worldwide is beginning to integrate AI-related competencies into undergraduate curricula through simulation platforms, adaptive learning systems, and AI-assisted tutoring tools [,]. Despite the growing global momentum toward AI integration in healthcare education, substantial disparities persist in students’ preparedness, confidence, and practical use of AI technologies. Studies conducted among medical and health sciences students indicate that while learners generally demonstrate positive attitudes toward AI and recognize its potential value in healthcare, their knowledge and structured training remain limited [,]. Many students report relying on informal sources such as peer networks, online tutorials, or social media to learn about AI due to the absence of formal curricular integration []. This gap between interest and structured training raises concerns regarding the safe and responsible application of AI in clinical education and professional practice. Emerging evidence also indicates that high levels of AI use among students do not necessarily correspond with adequate understanding of its capabilities and limitations. Recent studies suggest that generative AI tools are frequently used for academic writing, summarization, and information retrieval rather than for deeper clinical reasoning or evidence synthesis []. Scholars have warned that unsupervised reliance on AI systems may increase the risk of misinformation, algorithmic bias, and diminished clinical reasoning if students lack the critical skills necessary to evaluate AI-generated outputs []. Therefore, integrating AI literacy into health sciences education is increasingly considered essential for ensuring responsible and effective use of digital health technologies.