Effectiveness of AI-based interventions in workplace mental health: a systematic review and narrative synthesis
Year: 2026
Type: review
Study design: Systematic Review
Abstract: BACKGROUND: Workplace mental health is a growing global priority. Traditional approaches to intervention delivery often face barriers of scalability and engagement. Recent advances in artificial intelligence (AI) offer new opportunities for dynamic, personalized support, but their effectiveness and implementation in occupational settings remain unclear. SOURCES OF DATA: This systematic review included 17 studies published between 2018 and 2024, identified from six databases. Studies were appraised using Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines, and risk of bias was assessed with Cochrane Risk of Bias 2.0 (RoB 2.0) and ROBINS-I tools. AREAS OF AGREEMENT: AI-based interventions, such as chatbot using cognitive behavioural therapy and predictive analytics, show promise for improving worker's mental health, enhancing resilience, and improving engagement. Acceptability was generally high across studies. AREAS OF CONTROVERSY: Despite positive findings, intervention maturity remains low, and outcome reporting is inconsistent. Few studies systematically addressed adverse events, rollout scalability, or ethical concerns, and the added value of AI over traditional approaches is uncertain. GROWING POINTS: AI interventions may offer flexible, adaptive solutions for improving workplace mental health, with strong engagement indicators. There is a pressing need to support clinicians and occupational health teams in evaluating potentially useful AI tools. AREAS TIMELY FOR DEVELOPING RESEARCH: Future research must prioritize high quality randomized trials, long-term follow-up, and real-world implementation studies. Standardized frameworks for reporting effectiveness, harms, and ethical considerations are important for safe, trustable, and sustainable adoption in occupational health.
Source: British Medical Bulletin
DOI: 10.1093/bmb/ldag007
Institution: King's College London
Language: English
Subfield: Applied Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,362
OpenAlex ID: W7128637358