The human brain creates the dynamic and complex system of human consciousness, which has long defied the conventional, symptom-based diagnostic approaches used in psychiatry. Traditional conceptual and diagnostic models, while useful for categorizing mental disorders, can have important shortcomings in that they often do not capture the complexity or neurobiological mechanisms that underlie psychiatric conditions. Artificial intelligence (AI) has the potential to transform our understanding and treatment of mental health, through the integration of multimodal datasets—ranging from genomic profiles to neural circuitry characteristics to digital monitoring—in order to identify actionable biomarkers and design more patient-specific treatment plans that can lead to improved outcomes. This perspective review summarizes the origins of AI applications to mental health and highlights the potential impact of AI on numerous facets of mental health care, spanning psychodiagnostics, functional neuroimaging, genomic-informed personalized pharmacology, closed-loop neuromodulatory technologies, and digital therapeutics. It also discusses ethical, regulatory, and societal challenges likely to arise with the increasing applications of AI in the mental health field.
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