Perspectives of Health Care Providers about the Use of Artificial Intelligence in Mental Health Care: An Integrative Review
Bibliographic record
Abstract
Understanding the health care providers’ perspectives is essential for successful deployment of artificial intelligence (AI) in health care. In this integrative review, we explored the health care providers’ perspectives about the use of AI for addressing mental health issues. We searched the MEDLINE, EMBASE, CINAHL, and PsycINFO from outset through November 2023. The following themes were identified from eight included studies: anticipating a forthcoming shift; AI acceptability; AI literacy; perceived capacity of AI for providing mental health care; potential benefits; and ethical and legal considerations. Findings showed that the providers’ perspectives about the AI capacity to deliver mental health care varied substantially across clinical tasks. Several apprehensions emerged regarding ethical, legal, and regulatory aspects of the technology use. Clarification of regulatory and ethical issues, transparency in model development, improved AI literacy, and involvement of all relevant stakeholders in development process can facilitate realistic, receptive, and safe deployment of the technology in clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".