Evidence map›Paper›PMID 40106346›Full record

ArticleJMIR human factors2025

AI Chatbots for Psychological Health for Health Professionals: Scoping Review.

Gumhee Baek, Chiyoung Cha, Jin-Hui Han

Abstract readScoping Review
In one paragraph

Article in JMIR human factors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
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  5. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Gumhee Baek *College of Nursing, Ewha Womans University, 52 Ewhayeodae-gil, Daehyun-dong, Seodaemun-gu, Seoul, 03760, Republic of Korea, 82 1035065701.ORCID 0000-0003-1999-0158
Chiyoung Cha *College of Nursing, Ewha Womans University, 52 Ewhayeodae-gil, Daehyun-dong, Seodaemun-gu, Seoul, 03760, Republic of Korea, 82 1035065701.ORCID 0000-0003-0115-1348
Jin-Hui Han *College of Nursing, Ewha Womans University, 52 Ewhayeodae-gil, Daehyun-dong, Seodaemun-gu, Seoul, 03760, Republic of Korea, 82 1035065701.ORCID 0000-0003-1015-1817

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceHealth PersonnelGenerative Artificial IntelligenceHumansAI chatbotartificial intelligenceburnouthealth professionalspsychological healthscoping review

Identifiers

PMID40106346
PMCPMC11939020

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.