ReviewDiagnostics (Basel, Switzerland)2022
Challenges for Artificial Intelligence in Recognizing Mental Disorders.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring depression in adults over a decade: a review of longitudinal studies.BMC psychiatry · 2025Pooled it
- AI in Mental Health: Transforming Diagnosis and Management of Depression and Anxiety.Health science reports · 2026Article
- WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis.NPJ digital medicine · 2026Article
- Uncovering the molecular mechanism of solasonine in treating interstitial cystitis via network pharmacology and molecular docking.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Generative AI as interactional infrastructure for meaning-centered care in later life.Frontiers in psychiatry · 2026Article
- A generative two-stage semantic intermediary framework for explainable mental health early warning in higher education.Frontiers in psychiatry · 2026Review
- A controlled trial examining large Language model conformity in psychiatric assessment using the Asch paradigm.BMC psychiatry · 2025Article
- An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.Scientific reports · 2025Article
- Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection.BMC psychiatry · 2025Article
- The Use of AI in Mental Health Services to Support Decision-Making: Scoping Review.Journal of medical Internet research · 2025Article
- Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study.PloS one · 2025Article
- Neuropsychiatric symptoms in mild cognitive impairment and early Alzheimer's disease: Clinical pattern and diagnostic implications.AIMS neuroscience · 2025Review
- Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry.Brain sciences · 2024Article
- Toward molecular diagnosis of major depressive disorder by plasma peptides using a deep learning approach.Briefings in bioinformatics · 2024Article
- Ethical trade-offs in AI for mental health.Frontiers in psychiatry · 2024Article
- Artificial intelligence in mental healthcare: transformative potential vs. the necessity of human interaction.Frontiers in psychology · 2024Article
- Beyond clinical observations: a scoping review of AI-detectable observable cues in borderline personality disorder.Frontiers in psychiatry · 2024Article
- Psychotherapy, artificial intelligence and adolescents: ethical aspects.Journal of preventive medicine and hygiene · 2023Article
- Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- AI for Psychometrics: Validating Machine Learning Models in Measuring Emotional Intelligence with Eye-Tracking Techniques.Journal of Intelligence · 2023Article
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
Abstract
Artificial Intelligence (AI) appears to be making important advances in the prediction and diagnosis of mental disorders. Researchers have used visual, acoustic, verbal, and physiological features to train models to predict or aid in the diagnosis, with some success. However, such systems are rarely applied in clinical practice, mainly because of the many challenges that currently exist. First, mental disorders such as depression are highly subjective, with complex symptoms, individual differences, and strong socio-cultural ties, meaning that their diagnosis requires comprehensive consideration. Second, there are many problems with the current samples, such as artificiality, poor ecological validity, small sample size, and mandatory category simplification. In addition, annotations may be too subjective to meet the requirements of professional clinicians. Moreover, multimodal information does not solve the current challenges, and within-group variations are greater than between-group characteristics, also posing significant challenges for recognition. In conclusion, current AI is still far from effectively recognizing mental disorders and cannot replace clinicians' diagnoses in the near future. The real challenge for AI-based mental disorder diagnosis is not a technical one, nor is it wholly about data, but rather our overall understanding of mental disorders in general.
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.