Hybrid Models in Digital Psychiatry: Toward Blended Human–AI Care

Mental disorders are a leading cause of global disability, yet many countries face shortages of mental-health professionals and limited access to timely, high-quality care.Advances in artificial intelligence (AI), including large language models, multimodal sensing, and digital phenotyping, offer potential solutions.However, concerns regarding safety, bias, privacy, and crisis management limit the use of AI as an autonomous clinical tool.This narrative review synthesizes evidence from 40 peer-reviewed studies, including randomized trials, behavioral sensing research, AIenabled clinical decision-support systems, and international regulatory frameworks.Across conversational agents, telepsychiatry augmentation, diagnostic algorithms, and large-scale implementations, AI improved scalability, enabled earlier detection of symptom changes, and supported personalized treatment when integrated within cliniciansupervised workflows.In contrast, fully autonomous systems showed risks such as hallucinated outputs, demographic bias, inconsistent crisis detection, and weak data governance.Hybrid digital psychiatry, combining AI with clinician oversight, appears to be the most clinically viable and ethically grounded approach.