SynthesisJMIR mental health2024
Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis.
Synthesis in JMIR mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Healthcare Professionals' Perceptions of Artificial Intelligence in Healthcare-A Systematic Review of Qualitative Studies.Journal of advanced nursing · 2026Pooled it
- Leveraging artificial intelligence in the prediction, diagnosis and treatment of depression and anxiety among perinatal women in low- and middle-income countries: a systematic review.BMJ mental health · 2025Pooled it
- Artificial Intelligence Applications in Mental Health: A Systematic Review of Clinical Practice, Educational Transformation, and Ethical Governance.Healthcare (Basel, Switzerland) · 2026Review
- The Challenges of AI Applications in Health Care: Qualitative Study.Journal of medical Internet research · 2026Article
- Deployment of a cloud-based passive defecation monitoring system for continuous gut health monitoring.Nature protocols · 2026Review
- Views of People With Psychosis About Algorithm-Based Relapse Prediction and Data Sharing: Qualitative Study.Journal of medical Internet research · 2026Article
- Mental Health Professionals' Perspectives on Digital Remote Monitoring in Services for People with Psychosis.Schizophrenia bulletin · 2026Article
- Artificial Intelligence for Mental Health Monitoring: A Solution for Digital Behavioral Health Care and Education-An Umbrella Review.Health science reports · 2026Review
- Artificial intelligence in psychiatry: clinical applications, limitations, and ethical challenges.Frontiers in behavioral neuroscience · 2026Article
- Cultural validation of the RCADS and use of ensemble learning for symptom profiling of anxiety and depression.Frontiers in psychiatry · 2026Article
- Weighing Costs and Benefits of Delay and the Acceptance of Two Decision Support Tools in Mental Health Care: Scoping Study Using Quantitative and Qualitative Data.JMIR human factors · 2025Article
- Practitioner Perspectives on the Uses of Generative AI Chatbots in Mental Health Care: Mixed Methods Study.JMIR human factors · 2025Article
- Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review.Journal of medical Internet research · 2025Article
- Smartphone as a Sensor in mHealth: Narrative Overview, SWOT Analysis, and Proposal of Mobile Biomarkers.Sensors (Basel, Switzerland) · 2025Review
- Mental Health Professionals' Technology Usage and Attitudes Toward Digital Health for Psychosis: Comparative Cross-Sectional Survey Study.JMIR mental health · 2025Article
- Healthcare professionals' views on implementing digital health tools in psychosis: a national survey in the UK.BMJ digital health & AI · 2025Article
- Multi-physiological signal fusion for objective emotion recognition in educational human-computer interaction.Frontiers in public health · 2024Article
- Remote sensing mental health: A systematic review of factors essential to clinical translation from validation research.Digital healthReview
- Article
- Artificial intelligence in psychiatry: A systematic review and meta-analysis of diagnostic and therapeutic efficacy.Digital healthReview
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 at 2 institutions in 1 country.
Funding
Abstract
backgroundMental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation.
objectiveThis study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care.
methodsA systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework.
resultsOverall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues.
conclusionsAlthough clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered.
trial registrationPROSPERO International Prospective Register of Systematic Reviews CRD42022331698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=331698.
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.