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Machine Learning in Healthcare · Journal article
International Journal of Innovative Technologies in Social Science · August 22, 2026
A consensus or society position rather than new primary data.
This narrative review surveys AI and machine learning approaches to psychiatric diagnosis, risk stratification, and monitoring across multiple data modalities (neuroimaging, speech, text, wearables, social media). While computational models show promise in detecting signatures associated with depression, schizophrenia, bipolar disorder, anxiety, and suicide risk, the review identifies critical gaps—including insufficient and unrepresentative datasets, overfitting, poor external validation, low interpretability, privacy concerns, algorithmic bias, and regulatory uncertainty—that position AI currently as a supportive adjunct rather than a replacement for clinical judgment.
Narrative review. Literature on AI applications in psychiatric assessment, diagnosis, and monitoring; no specific patient cohort enrolled.. Intervention: Artificial intelligence techniques (machine learning, deep learning, natural language processing, digital phenotyping, large language models, multimodal modelling) applied to psychiatric assessment..
Computational models can detect valuable signatures in electronic health records, neuroimaging, speech, clinical text, smartphone usage, wearable sensors, and social media. AI techniques can aid in identification of depression, schizophrenia, bipolar disorder, anxiety disorders, and suicide risk. Multimodal approaches are highlighted as particularly promising because they integrate biological, psychological, and social aspects of mental illness.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
Clinicians should view AI-assisted psychiatric assessment as a supportive tool for enhanced data interpretation and risk flagging, not as a replacement for clinical diagnosis or judgment. Implementation requires resolution of data quality, validation, bias, and regulatory issues before widespread clinical adoption.
A narrative review synthesizing current AI applications in psychiatry, identifying both clinical potential and substantial methodological limitations that preclude practice-changing recommendations.
As stated by the source record.
Clinicians should view AI-assisted psychiatric assessment as a supportive tool for enhanced data interpretation and risk flagging, not as a replacement for clinical diagnosis or judgment. Implementation requires resolution of data quality, validation, bias, and regulatory issues before widespread clinical adoption.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Artificial intelligence is being considered as a means of assisting psychiatric diagnosis, risk prediction, and long-term monitoring. Such applications in psychiatry have clinical relevance due to the fact that most psychiatric diagnosis is still based on interview, observation, and self-report, and also because it relies on symptom-based categorization that suffers from overlapping symptoms, tardiness in detection of illness, and discrepancies among patients given a single diagnosis. In this narrative review, we discuss applications of artificial intelligence in psychiatric assessment including machine learning, deep learning, natural language processing, digital phenotyping, large language models, and multimodal modelling. Papers published predominantly from 2020-2025 were included, with preference given to systematic reviews and meta-analyses, multicenter trials, and clinically significant publications. It is evident that various computational models can detect valuable signatures in data obtained from electronic health records, neuroimaging, speech, clinical text, smartphone usage, wearable sensors, and social media. Such techniques can aid in the identification of depression, schizophrenia, bipolar disorder, anxiety disorders, and suicide risk. Multimodal approaches are particularly interesting due to the fact that they take the biological, psychological and social aspects of mental illness into consideration. On the other hand, the domain is currently suffering from insufficient and non-representative data sets, over-fitting, poor external validation, low interpretability, privacy issues, algorithmic bias, and unclear regulations. These aspects suggest that AI should be viewed as a supportive tool and not a replacement for the clinician.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.