AI Applications Integrating Legal and Regulatory Perspectives in Mental Health: Systematic Review

Abstract Background Artificial intelligence (AI) offers new methods to improve diagnosis and treatment in mental health. However, its use raises legal and ethical concerns. Objective AI is increasingly being used for mental health care, but its clinical prominence and ethical implications are yet to be determined. This systematic review discusses the clinical efficacy and the ethical issues of AI in mental health treatment and is trying to focus on the main conclusions with regard to the diagnostic accuracy and the therapeutic efficacy. Methods The review encompasses an exhaustive analysis of 35 studies in the narrow time frame of 2013‐2024. It allows for multidatabase exploration and follows the systematic and well-established practice of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. This review searched PubMed (biomedical emphasis), IEEE Xplore (engineering or AI), PsycINFO (psychological literature), Scopus (multidisciplinary focus), and Cochrane Library (evidence-based treatment) from January 1, 2013, to December 31, 2024. Studies include those that focused on AI applications for diagnosis, treatment, or patient engagement, excluding tangential uses (eg, administrative tasks). Only English-language publications were searched to mitigate language bias, though this introduces potential geographic bias. Results AI-enabled interventions of natural language processing models showed up to 89% accuracy for depression detection. The wearables, as in the Empatica E4, showed an F 1 -score of 0.81 to predict anxiety episodes. AI-enabled therapies, such as chat-based interventions and online cognitive behavioral therapy, have been shown to improve the anxiety symptoms of about 30% in some studies; however, there was considerable variability in the impact based on study design, intervention duration, and comparator conditions, as well as the overall methodological quality of the studies. However, challenges remain, such as including biases in training data, evidenced by performance declines of up to 15% in non-English datasets, and concerns over data privacy. Conclusions In addressing mental health, AI has the potential to revolutionize mental health treatment, offering cost-saving, personalized, and culturally sensitive interventions while protecting privacy, equity, and human agency.