ReviewCurrent psychiatry reports2024
Machine Learning for Mental Health: Applications, Challenges, and the Clinician's Role.
Review in Current psychiatry reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis 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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Factors associated with psychological distress among family caregivers of preschool children with autism: an analysis.Frontiers in psychiatry · 2026Article
- Great debate: artificial intelligence will replace much of what cardiologists do.European heart journal · 2025Article
- Ethical decision-making for AI in mental health: the Integrated Ethical Approach for Computational Psychiatry (IEACP) framework.Psychological medicine · 2025Review
- A mathematical framework for modelling the dynamic nature of ADHD symptoms.Frontiers in psychiatry · 2025Article
- Perspective on Using Artificial Intelligence in Alcohol Research and Treatment: Opportunities and Ethical Considerations.Alcohol research : current reviews · 2025Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewThis review aims to evaluate the current psychiatric applications and limitations of machine learning (ML), defined as techniques used to train algorithms to improve performance at a task based on data. The review emphasizes the clinician's role in ensuring equitable and effective patient care and seeks to inform mental health providers about the importance of clinician involvement in these technologies. RECENT
findingsML in psychiatry has advanced through electronic health record integration, disease phenotyping, and remote monitoring through mobile applications. However, these applications face challenges related to health equity, privacy, translation to practice, and validation. Clinicians play crucial roles in ensuring data quality, mitigating biases, promoting algorithm transparency, guiding clinical implementation, and advocating for ethical and patient-centered use of ML tools. Clinicians are essential in addressing the challenges of ML, ensuring its ethical application, and promoting equitable care, thus improving the effectiveness of ML in practice.
Indexed as
Identifiers
39523249What 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.