Transforming mental health research and care through artificial intelligence.

Artificial intelligence (AI) holds transformative potential for the care of people with mental health illnesses. This Review explores key domains and emerging applications of AI in mental health, emphasizing the challenges that must be addressed to ensure safe, effective, and sustainable clinical integration. Distinctive features of mental health care—such as the lack of objective biomarkers, the reliance on behavioral and emotional assessments, the legacy of stigma, and the importance of privacy—present fundamental challenges to overcome. We argue that the clinical translation of AI tools should be approached through the lens of an individual patient's journey from the onset of symptoms through diagnosis, treatment, recovery, and emotional well-being. Editor's summary: Integrating artificial intelligence (AI) into routine mental health care is hotly contested. Although younger generations are more receptive, others warn that the new technologies are not equipped to safely provide care and empathy for culturally diverse populations. Incidents of conversational AI goading users to engage in self-harm or make reckless decisions reinforce such concerns. However, access to affordable, personalized mental health care remains a global crisis. Opel and Breakspear review ways in which AI may reduce care inequities when deployed responsibly. They highlight AI tools including smartwatches, mobile devices, and chatbots used across stages of care: during pretreatment to monitor behavior, emotions, and symptom patterns; during treatment to support clinicians in diagnosing illness and implementing strategies; and after treatment to prevent relapse and track recovery. Although promising, whether therapeutic AI is ready for prime time is debatable. —Ekeoma Uzogara BACKGROUND: Artificial intelligence (AI) offers transformative potential to disrupt and improve mental health research and care. AI-driven clinical decision support systems are on the verge of widespread integration into routine mental health practice. This opportunity aligns with the pressing need to address existing therapeutic gaps and to relieve overburdened mental health services. However, the field presents distinctive challenges that distinguish it from other areas of medicine. Key among these challenges is the absence of objective biomarkers, with diagnosis and treatment heavily reliant on subjective appraisals of behavior and affect—areas that hold substantial promise for AI-based applications. Additionally, mental health care involves some of the most intimate aspects of personal information and hinges on the critical relationship between patients and their caregivers. This dynamic necessitates careful consideration of issues surrounding stigma, privacy, and trust to ensure the ethical and effective implementation of AI technologies. ADVANCES: AI-driven technologies in mental health research have used machine learning to integrate complex, high-dimensional biological data, such as imaging, genetics, and molecular signaling, to improve the accuracy of diagnosis and prognosis in mental disorders. However, the replicability, scalability, and generalizability of such methods across diverse sites remain substantial hurdles. This issue of scalable AI-driven applications for mental health can be partially addressed by using AI to monitor behavior and emotional states in real-life settings. Technologies such as smartphones and wearables enable unprecedented insights into daily behaviors across diverse environments, both within and beyond treatment contexts. These innovations allow researchers to examine factors such as mood and behavior at high temporal resolution while introducing entirely new data dimensions, such as location-based features, which could revolutionize phenotyping in mental health. Furthermore, digital interventions can now be applied, scaled, and personalized using AI-driven algorithms. Chatbots powered by large language models are at the forefront of growing interest in this area, with early tools demonstrating efficacy in both mental health treatment and clinical decision support. Despite these promising advances, substantial research is still needed to facilitate their integration into routine care. This includes evaluating patient and clinician adherence; addressing ethical, safety, and privacy concerns; and ensuring cost-effectiveness. OUTLOOK: The successful translation of AI into clinical practice will require robust validation and multisite generalization of promising applications in real-world settings. Achieving this goal in mental health necessitates a unified effort and consensus on the collection and use of relevant data entities across research and care. Scalable digital technologies that facilitate the seamless collection of diagnostically relevant information in routine care could serve as a critical foundation. However, these technologies must be thoughtfully integrated into treatment plans and therapeutic contexts to ensure adherence and long-term success. AI could be used at various stages—pretreatment (for screening or triage), during treatment (to assist clinicians in adapting therapeutic strategies or provide real-time feedback during sessions), or posttreatment (for relapse prevention and long-term recovery monitoring). Future research should focus on empowering patients, training caregivers, and conceptualizing treatment strategies that effectively incorporate AI technologies. Ensuring that AI solutions are centered on the needs and priorities of patients as they traverse mental health challenges will be critical to optimize the benefits that AI-based solutions can provide. AI's potential to transform mental health research and care.: Multimodal data from established sources plus emerging AI-enabled tools improve phenotypic characterization, which supports diagnosis and personalized treatment. Clinical decision support systems, therapeutic chatbots, and digital monitoring create new opportunities for tailored care. Successful clinical translation of this AI-augmented care will depend on rigorous validation, stakeholder engagement, strict guardrails, and clinician education. [ABSTRACT FROM AUTHOR]