Mental disorders constitute an urgent and escalating global public-health concern. Recent advances in artificial intelligence (AI) have begun to transform both psychiatric theory and clinical practice, generating unprecedented opportunities for precision diagnosis, mechanistic insight and personalized intervention. Here, we present a narrative review that examines the current landscape of AI-enhanced psychiatry, evaluates AI's capacity to refine diagnostic nosology, elucidate etiological mechanisms, formalize diagnostic criteria and optimize treatment strategies, and delineates the concomitant ethical, legal and social challenges–most notably those arising from data privacy, algorithmic bias and inequitable access to technological resources. In parallel, the review interrogates the implications of this technological inflection point for medical education. It argues that contemporary training programs must evolve through systematic curricular re-design, the incorporation of computational and data science competencies, the adoption of integrative pedagogical models that couple theoretical instruction with hands-on algorithmic practice, and the reinforcement of bioethical literacy. Only by embedding AI fluency within a robust framework of humanistic and professional values can the next generation of psychiatrists be equipped to harness algorithmic tools responsibly and to translate their outputs into clinically meaningful decisions.
Zheng et al. (2025) studied this question.