ArticleSchizophrenia bulletin2026
Analyzing Generative AI and Machine Learning in Auto-Assessing Schizophrenia's Negative Symptoms.
Article in Schizophrenia bulletin, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Large Language Models and Their Applications in Mental Health: Scoping Review.JMIR mental health · 2026Article
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Emerging Neurobiological and Therapeutic Insights into Schizophrenia: A Comprehensive Review.International journal of molecular sciences · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
background and hypothesisTraditional assessments of schizophrenia's negative symptoms rely on subjective and time-consuming psychiatric interviews. To provide more objective and efficient evaluations, this study examines the efficacy of an automated system utilizing generative AI (GenAI) and machine learning (ML) to assess negative symptoms of schizophrenia, including expression (EXP) and motivation and pleasure (MAP) domains. STUDY
designA semi-structured interview protocol based on the Clinical Assessment Interview for Negative Symptoms was used to conduct interviews with schizophrenia patients. An experienced senior psychiatrist carried out these interviews, which were audio- and video-recorded, at the National Taiwan University Hospital between July 2022 and August 2023. An ML-based system analyzed visual and audio data for EXP assessment, while GenAI analyzed interview transcripts for MAP assessment. STUDY
resultsThe study cohort consisted of 69 males and 91 females with a mean age of 41.68 years (SD = 10.46). The ML-based EXP assessment showed moderate to substantial reliability, with an intraclass correlation coefficient (3, 1) (ICC3,1) of 0.65 and a weighted kappa of 0.62. The GenAI-based MAP assessment demonstrated good reliability, with an ICC3,1 of 0.82 and a weighted kappa of 0.77. The system achieved strong linear correlations with clinician ratings (Pearson's correlation coefficient ≥ 0.54) and maintained low error rates (mean absolute error ≤ 0.81; root mean square error ≤ 1.16) for each assessment item.
conclusionsThe study demonstrates the efficacy of GenAI and ML in the automated assessment of schizophrenia's negative symptoms, highlighting their potential to enhance the consistency and efficiency of clinical evaluations.
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