The editorial emphasizes the importance of screening for mental disorders, which faces obstacles such as lack of personnel, time constraints, limited access to services, and low-quality self-assessment of internal states due to memory reconstruction, inattention, social desirability, or cognitive heuristics[1]. Advances in big data, artificial intelligence and technology are opening up new possibilities for screening and supporting the diagnosis of mental disorders[1]. In primary care, explainable AI (explainable artificial intelligence) offers accessible and interpretable tools that adapt to doctors' workflows, are cost-effective, efficient and easy to regularly monitor at-risk individuals[1]. These tools translate existing knowledge into technologically supported screening, demystifying complex algorithms and enabling informed decisions[1]. Explainable AI supports interdisciplinary collaboration between general practitioners and mental health specialists[1]. The Research Topic collects studies on advances in explainable AI for the screening and diagnosis of mental disorders, from basic research to implementation in healthcare[1]. It includes areas such as marker evaluation, algorithm construction and validation, applications in practice, integration into primary care, comparison with psychometric instruments, and addressing comorbidities in chronically ill patients[1].