aceBarriers and Facilitators to the Use of Large Language Model-Based Conversational Agents in Mental Healthcare: A Systematic Review
(1) Background/Objectives: Over one billion individuals globally live with mental health conditions, yet the treatment gap exceeds 75% in low- and middle-income countries. Large language model (LLM)-based conversational agents have emerged as a potentially scalable solution, though the evidence base remains nascent and largely pre-clinical. This review synthesises barriers and facilitators to their implementation in mental healthcare using the Consolidated Framework for Implementation Research (CFIR). (2) Method…
LLM-based conversational agents offer 24/7 availability as a scalable way to help address the mental health treatment gap.
LLM-based conversational agents in mental healthcare frequently show inadequate crisis detection, creating critical safety deficiencies.
Evidence base is nascent and largely pre-clinical, and reported frequencies are study-level counts not population prevalence, with major gaps in implementation process evaluation.
Evidence
- Peer-reviewedHealthcare2026-05-07
- Peer-reviewedWorld Psychiatry2025-05-15
- Peer-reviewedJournal of Medical Internet Research2025-04-25
- Peer-reviewedJournal of Mental Health & Clinical Psychology2025-09-05
- Peer-reviewedJournal of Mental Health & Clinical Psychology2025-09-05
How should this claim be treated?
Truvace Impact Record TRV-2026-0183, v5: “Barriers and Facilitators to the Use of Large Language Model-Based Conversational Agents in Mental Healthcare: A Systematic Review.” Truvace, 2026-09-24. /record/TRV-2026-0183 (accessed at citation time). sha256 57be1c798f56a8b2…
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