ReviewPatterns (New York, N.Y.)2022
Modern views of machine learning for precision psychiatry.
Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
88 citing papers in PubMed, 2 syntheses or guidelines pooled it, 160 citations in OpenAlex.
- Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Speech analysis and speech emotion recognition in mental disease: a scoping review.Frontiers in psychology · 2025Pooled it
- Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.JMIR mental health · 2025Trial
- Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression.Molecular psychiatry · 2023Trial
- Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges.Psychiatric research and clinical practice · 2026Article
- Towards scalable biomarker discovery in posttraumatic stress disorder: triangulating genomic and phenotypic evidence from a health system biobank.Molecular psychiatry · 2026Article
- Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and Validation Study.JMIR mHealth and uHealth · 2026Article
- The use of artificial intelligence (AI) in neuropsychiatric drug discovery: current challenges and future directions.Translational psychiatry · 2026Review
- A Narrative Review of Ethical Issues in Precision Psychiatry: Mapping Unresolved Tensions Across Modalities.Journal of personalized medicine · 2026Review
- From the Pain Matrix to Functional Networks: A Narrative Review of Chronic Pain Mechanisms Across Adult and Pediatric Populations with Emerging AI Perspectives.Brain sciences · 2026Review
- Decision processes in 3D structural MRI schizophrenia classification evaluated with saliency maps.Scientific reports · 2026Article
- Review
- Construction and Verification of a Risk Prediction Model for Suicidal Ideation in Patients With Bipolar Disorder: A Machine Learning Analysis.Alpha psychiatry · 2026Article
- Review
- The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014-2024).Neuroinformatics · 2026Review
- Rethinking language, cognition and assessment in psychosis: How bilingualism challenges psychiatry and how natural language processing can help.Schizophrenia (Heidelberg, Germany) · 2026Review
- A cautionary tale for AI and machine learning in psychiatry.Translational psychiatry · 2026Review
- A functional system-informed graph neural network framework to quantify interpretable brain dysfunction in ASD.Neural networks : the official journal of the International Neural Network Society · 2026Article
- Explainability in AI-enabled medical neurotechnology: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Psychopathology profiles and longitudinal correlates of nonsuicidal self-injury in youth: a machine-learning approach.Translational psychiatry · 2026Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.