The field of mental health care is undergoing rapid transformation driven by technological innovations and neuroscientific insights, with interpretable artificial intelligence (AI) emerging as a pivotal force shaping research and clinical practice. Traditional models of diagnosis and risk assessment are often constrained by subjective measures and the “black box” nature of conventional AI approaches, which can obscure underlying psychiatric mechanisms and contribute to variability in care quality. In recent years, AI technologies have demonstrated remarkable potential in enhancing the precision of neuropsychopharmacological research, improving predictive modeling of cognitive impairment, and enabling the design of personalized therapeutic interventions informed by pharmacogenetic factors. Notably, interpretable machine learning, natural language processing, and digital phenotyping have facilitated granular analysis of patient data, yet the integration of these approaches with neuroscience-based validation and their clinical translation remains an ongoing challenge. Despite significant advances, continued debate persists regarding ethical implications, data security, transparency, and generalizability of AI-facilitated applications, underscoring the need for further multidisciplinary inquiry into explainable and mechanism-oriented models.

This Research Topic aims to explore how interpretable AI can be leveraged to advance mental health care by bridging the gap between data science and neuroscience. The principal goals are to identify AI-driven approaches for understanding psychiatric mechanisms, evaluate the efficacy of AI in neuropsychopharmacological research, and address the practical barriers to widespread adoption of transparent AI systems. Research contributing to these aims may address the accuracy of AI models in detecting neurodevelopmental or cognitive changes, mechanisms for integrating AI into clinical pharmacology, or strategies for maximizing patient trust through model interpretability.

To gather further insights into the dynamic integration of interpretable AI within neuropsychiatric research and clinical applications, we welcome articles addressing, but not limited to, the following themes:

o AI and neuroscience-based assessment and behavioral analysis in mental health
o Interpretable AI for identifying psychiatric mechanisms and biomarkers
o Neuropsychopharmacological impact and predictive modeling of treatment response using AI
o Pharmacogenetic factors and AI-powered personalized care protocols
o Ethical, legal, transparency, and clinical validity considerations in mechanism-oriented psychiatric AI
o Implementation of interpretable AI models in clinical settings to improve patient safety
o AI-driven social support systems: examining their impact on neural mechanisms and recovery in mental disorders

We accept submissions as Original Research, Systematic Reviews, Mini-Reviews, Reports, Perspectives, and Commentary articles.

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Interpretable AI and Neuroscience in Mental Disorders: Mechanisms and Advances in Neuropsychopharmacology