ReviewNPJ digital medicine2023
Explainable artificial intelligence for mental health through transparency and interpretability for understandability.
Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 5 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
83 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- Artificial intelligence in mental health care: a scoping review of reviews.Frontiers in psychiatry · 2026Pooled it
- The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.BMC medical informatics and decision making · 2025Pooled it
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Temporal epidemiology and multi-source forecasting of hemorrhagic fever with renal syndrome and leptospirosis in mainland China: An interpretable machine learning study.Preventive medicine reports · 2026Article
- Article
- Leveraging social media footprints for predicting college student anxiety: a machine learning approach.BMC psychology · 2026Article
- Towards trustworthy brain stroke diagnosis using a lightweight explainable deep learning framework for CT imaging.Scientific reports · 2026Article
- YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception.Scientific reports · 2026Article
- Enhancing healthcare explainability through multiobjective counterfactual explanations.Scientific reports · 2026Article
- Cross-Dataset Evaluation of an Automated Video-Based Model for Detecting Tardive Dyskinesia Using the Clinician's Tardive Inventory: Validation Study.JMIR mental health · 2026Article
- Explainable AI for Well-Being Prediction From Lifestyle Data: 2-Study Design.JMIR mental health · 2026Article
- Artificial Intelligence Virtual Organoids (AIVOs).Bioactive materials · 2026Review
- Towards a general-purpose foundation model for functional MRI analysis.Nature biomedical engineering · 2026Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- A cautionary tale for AI and machine learning in psychiatry.Translational psychiatry · 2026Review
- Extrinsic Trust as a Contractual Framework for Accountable AI in Health Care: Viewpoint.Journal of medical Internet research · 2026Article
- SHAP-based explainable machine learning analysis of reward-related neural connectivity to predict preadolescent irritability.Neuroimage. Reports · 2026Article
- Machine-actionable criteria chart the symptom space of mental disorders.NPJ digital medicine · 2026Article
23 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
4 authors.
Funding
Abstract
The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what "explainability" means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability meaning model-agnostic techniques that augment a complex model (with internal mechanics intractable for human understanding) with a simpler model argued to deliver results that humans can comprehend. Given the differing usage and intended meaning of the term "explainability" in AI and ML, we propose instead to approximate model/algorithm explainability by understandability defined as a function of transparency and interpretability. These concepts are easier to articulate, to "ground" in our understanding of how algorithms and models operate and are used more consistently in the literature. We describe the TIFU (Transparency and Interpretability For Understandability) framework and examine how this applies to the landscape of AI/ML in mental health research. We argue that the need for understandablity is heightened in psychiatry because data describing the syndromes, outcomes, disorders and signs/symptoms possess probabilistic relationships to each other-as do the tentative aetiologies and multifactorial social- and psychological-determinants of disorders. If we develop and deploy AI/ML models, ensuring human understandability of the inputs, processes and outputs of these models is essential to develop trustworthy systems fit for deployment.
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.