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  3. Using Ai To Detect Psychosis Relapse: Scoping Review.
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  3. Using Ai To Detect Psychosis Relapse: Scoping Review.

Related Experiment Video

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Using AI to Detect Psychosis Relapse: Scoping Review.

Lorenzo Ghelfi1, Jack Healy1, Francesco Piacenza2,3

  • 1Department of Psychiatry, Royal College of Surgeons in Ireland, Smurfit Building, Beaumont, Dublin, Co Dublin, D09 YD60, Ireland, 353 0832026617.

JMIR Mental Health
|June 16, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise for detecting psychosis relapse using digital phenotyping. However, current AI models vary in effectiveness and require larger studies and advanced methods for clinical use.

Keywords:
AIartificial intelligencedeep learningdigital phenotypingmachine learningpsychosisrelapseschizophreniascoping review

Related Experiment Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

  • Digital Health
  • Artificial Intelligence in Psychiatry
  • Computational Psychiatry

Background:

  • Psychotic disorders are a major cause of global disability, with frequent relapse.
  • Artificial intelligence (AI) offers potential for enhanced clinical monitoring in psychosis.
  • Early detection of relapse is crucial for managing psychotic disorders.

Purpose of the Study:

  • To systematically review and map the literature on AI-based methods for detecting relapse in psychotic disorders.
  • To identify and analyze AI approaches including machine learning, deep learning, and natural language processing.
  • To assess the current state and limitations of AI in psychosis relapse detection.

Main Methods:

  • A systematic search of PubMed, PsycINFO, and Embase databases was conducted.
  • Included studies were observational, RCTs, and quasi-experimental using AI for psychosis relapse detection.
  • Data extraction and narrative synthesis were performed by independent reviewers.

    • Main Results:

    • Ten studies utilized digital tools like smartphones, smartwatches, and social media for data collection.
    • Digital phenotyping via smartphones and wearables was the most common data collection method.
    • AI model efficacy varied significantly, with sensitivity from 0.25-0.77 and specificity from 0.06-0.88.

    Conclusions:

    • AI, particularly passive digital phenotyping, shows potential for psychosis relapse detection.
    • Personalized, individual-level AI modeling demonstrates promise but requires validation.
    • Future research needs larger cohorts, advanced AI methods (e.g., large language models), and collaborative efforts for clinical implementation.