{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T19:30:16Z","timestamp":1792438216751,"version":"4.5.0"},"reference-count":63,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Ment Health"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Psychotic disorder represents a leading cause of disability worldwide, and relapse in psychosis is common. Artificial intelligence (AI) is increasingly recognized as a method that could aid clinical monitoring for individuals experiencing psychosis.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This review aims to map the existing literature on AI-based approaches\u2014including machine learning, deep learning, and natural language processing\u2014used to detect relapse in individuals with psychotic disorders.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A systematic search strategy was conducted on PubMed, PsycINFO, and Embase up to January 7, 2026. Observational studies, randomized controlled trials, and quasi-experimental studies that used AI methods to detect relapse in psychosis were eligible for inclusion. Screening and data extraction procedures were conducted by at least 2 reviewers working independently. Findings were extracted, charted, and described using narrative synthesis based on data extraction and consensus meetings with the research team. The scoping review was prospectively registered with the Open Science Framework.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Relevant studies identified (N=10) included the use of digital tools such as smartphone- and smartwatch-based monitoring, ecological momentary assessment tools, social media activity, and internet searches. Digital phenotyping via smartphones and wearables emerged as the most common method for data collection. The efficacy of AI models varied with sensitivity (or recall) ranging from 0.25 to 0.77 and specificity (or precision) ranging from 0.06 to 0.88. The reported area under the receiver operating characteristic curve for models ranged from 0.63 to 0.78. AI models were heterogeneous across studies, and most study findings were not replicated.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This scoping review highlights both the promise and the current limitations of AI in psychosis relapse detection. Passive digital phenotyping research in the detection of psychosis relapse has progressed, and personalized approaches with individual-level modeling show promise; however, further studies need to include larger numbers of participants and should incorporate methods such as large language models. Future studies will require large collaborations aimed at delivering AI methods for use in real-world clinical practice.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/92192","type":"journal-article","created":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T19:50:05Z","timestamp":1776541805000},"page":"e92192-e92192","source":"Crossref","is-referenced-by-count":1,"title":["Using AI to Detect Psychosis Relapse: Scoping 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