AI Chatbots for Psychological Health for Health Professionals: Scoping Review
Bibliographic record
Abstract
Background: Health professionals face significant psychological burdens including burnout, anxiety, and depression. These can negatively impact their well-being and patient care. Traditional psychological health interventions often encounter limitations such as a lack of accessibility and privacy. Artificial intelligence (AI) chatbots are being explored as potential solutions to these challenges, offering available and immediate support. Therefore, it is necessary to systematically evaluate the characteristics and effectiveness of AI chatbots designed specifically for health professionals. Objective: This scoping review aims to evaluate the existing literature on the use of AI chatbots for psychological health support among health professionals. Methods: Following Arksey and O'Malley's framework, a comprehensive literature search was conducted across eight databases, covering studies published before 2024, including backward and forward citation tracking and manual searching from the included studies. Studies were screened for relevance based on inclusion and exclusion criteria, among 2465 studies retrieved, 10 studies met the criteria for review. Results: Among the 10 studies, six chatbots were delivered via mobile platforms, and four via web-based platforms, all enabling one-on-one interactions. Natural language processing algorithms were used in six studies and cognitive behavioral therapy techniques were applied to psychological health in four studies. Usability was evaluated in six studies through participant feedback and engagement metrics. Improvements in anxiety, depression, and burnout were observed in four studies, although one reported an increase in depressive symptoms. Conclusions: AI chatbots show potential tools to support the psychological health of health professionals by offering personalized and accessible interventions. Nonetheless, further research is required to establish standardized protocols and validate the effectiveness of these interventions. Future studies should focus on refining chatbot designs and assessing their impact on diverse health professionals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".