ArticleSchizophrenia bulletin2020
Using Machine Learning in Psychiatry: The Need to Establish a Framework That Nurtures Trustworthiness.
Article in Schizophrenia bulletin, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 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
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Responsible artificial intelligence integration framework for psychiatric guidelines.The international journal of neuropsychopharmacology · 2026Guideline
- Linguistic markers for identifying post-traumatic stress disorder and associated symptoms: a systematic literature review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry.Current neuropharmacology · 2023Pooled it
- Artificial Intelligence Applications in Mental Health: A Systematic Review of Clinical Practice, Educational Transformation, and Ethical Governance.Healthcare (Basel, Switzerland) · 2026Review
- Cross-linguistic benchmarking of NLP metrics for psychosis research.NPJ digital medicine · 2026Article
- Nine quick tips for trustworthy machine learning in the biomedical sciences.PLoS computational biology · 2025Article
- AI-Augmented Content Validation in Behavioral Research: Development and Evaluation of the RATER System.MIS quarterly : management information systems · 2025Article
- Ethics in digital phenotyping: considerations regarding Alzheimer's disease, speech and artificial intelligence.Journal of medical ethics · 2025Article
- Power and reproducibility in the external validation of brain-phenotype predictions.Nature human behaviour · 2024Article
- Interdisciplinary approach to identify language markers for post-traumatic stress disorder using machine learning and deep learning.Scientific reports · 2024Article
- Beyond rating scales: With targeted evaluation, large language models are poised for psychological assessment.Psychiatry research · 2024Review
- Power and reproducibility in the external validation of brain-phenotype predictions.bioRxiv : the preprint server for biology · 2023Article
- Semantic and Acoustic Markers in Schizophrenia-Spectrum Disorders: A Combinatory Machine Learning Approach.Schizophrenia bulletin · 2023Article
- Understanding mental health through computers: An introduction to computational psychiatry.Frontiers in psychiatry · 2023Review
- Generalisable machine learning models trained on heart rate variability data to predict mental fatigue.Scientific reports · 2022Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder.Scientific reports · 2022Article
- Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation.Schizophrenia bulletin · 2022Article
- Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies.Schizophrenia bulletin · 2022Article
- Promise and Provisos of Artificial Intelligence and Machine Learning in Healthcare.Journal of healthcare leadership · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid embracing of artificial intelligence in psychiatry has a flavor of being the current "wild west"; a multidisciplinary approach that is very technical and complex, yet seems to produce findings that resonate. These studies are hard to review as the methods are often opaque and it is tricky to find the suitable combination of reviewers. This issue will only get more complex in the absence of a rigorous framework to evaluate such studies and thus nurture trustworthiness. Therefore, our paper discusses the urgency of the field to develop a framework with which to evaluate the complex methodology such that the process is done honestly, fairly, scientifically, and accurately. However, evaluation is a complicated process and so we focus on three issues, namely explainability, transparency, and generalizability, that are critical for establishing the viability of using artificial intelligence in psychiatry. We discuss how defining these three issues helps towards building a framework to ensure trustworthiness, but show how difficult definition can be, as the terms have different meanings in medicine, computer science, and law. We conclude that it is important to start the discussion such that there can be a call for policy on this and that the community takes extra care when reviewing clinical applications of such models..
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.