ReviewHealth science reports2026
Artificial Intelligence for Mental Health Monitoring: A Solution for Digital Behavioral Health Care and Education-An Umbrella Review.
Review in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Human-centered AI in healthcare teams: integrating clinical oversight, EHR auditability, and reimbursement pathways for responsible adoption.Frontiers in digital health · 2026Review
- Artificial Intelligence for Mental Health Monitoring: A Solution for Digital Behavioral Health Care and Education-An Umbrella Review.Health science reports · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The global burden of mental health disorders continues to escalate, placing immense strain on healthcare systems already challenged by workforce shortages and systemic barriers. As traditional models struggle to meet rising demands, artificial intelligence (AI) has emerged as a promising tool for enhancing the detection and monitoring of psychological distress. Aim: This umbrella review aimed to synthesize high-level evidence on how AI is employed in mental health monitoring and assess its potential to support digital behavioral health care and education. Methods: Following PRISMA 2020 guidelines, the review integrated findings from 29 systematic reviews, scoping reviews, and meta-analyses published between 2013 and 2025. Studies were selected using a rigorous, multistage screening process and evaluated through the Joanna Briggs Institute appraisal tools. Thematic synthesis was applied to extract and organize recurring insights from diverse digital and clinical contexts. Results: The results revealed that AI technologies, including machine learning, natural language processing, wearable sensors, and chatbots, enhance diagnostic accuracy, predict crises, and improve access to care. AI's adaptability across mobile platforms, educational settings, and telehealth environments was particularly evident, showing promise for underserved and stigmatized populations. However, concerns around data privacy, algorithmic bias, and user trust were recurrent themes that demand ethical safeguards and transparent governance. Conclusion: AI is redefining how mental health care is delivered by enabling proactive, personalized interventions. While challenges remain, the responsible and inclusive deployment of AI offers a transformative pathway toward accessible, real-time mental health support that bridges gaps in traditional care systems.
Indexed as
Identifiers
What Socratic 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 Socratic graph.