Evidence map›Paper›PMID 40508960›Full record

ReviewHealthcare (Basel, Switzerland)2025

Telepsychiatry and Artificial Intelligence: A Structured Review of Emerging Approaches to Accessible Psychiatric Care.

Artem Bobkov, Feier Cheng, Jinpeng Xu, Tatiana Bobkova, Fangmin Deng, Jingran He, Xinyan Jiang, Dinislam Khuzin, Zheng Kang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 3 pooled it
–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

5 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
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

9 authors.

Artem BobkovSchool of Health Management, Harbin Medical University, Harbin 150081, China.ORCID 0000-0002-8236-2341
Feier ChengSchool of Health Management, Harbin Medical University, Harbin 150081, China.
Jinpeng XuSchool of Health Management, Harbin Medical University, Harbin 150081, China.
Tatiana BobkovaDepartment of Rheumatology and Immunology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China.ORCID 0009-0008-8897-0912
Fangmin DengSchool of Health Management, Harbin Medical University, Harbin 150081, China.
Jingran HeSchool of Health Management, Harbin Medical University, Harbin 150081, China.
Xinyan JiangSchool of Health Management, Harbin Medical University, Harbin 150081, China.
Dinislam KhuzinDepartment of General Chemistry, Bashkir State Medical University, Ufa 450000, Russia.
Zheng KangSchool of Health Management, Harbin Medical University, Harbin 150081, China.

Funding

National Natural Science Foundation of China 72074064, 71573068
6 · The paper itself

Abstract

BACKGROUND/

objectivesArtificial intelligence is rapidly permeating the field of psychiatry. It offers novel avenues for the diagnosis, treatment, and prediction of mental health disorders. This structured review aims to consolidate current approaches to the application of AI in telepsychiatry. In addition, it evaluates their technological maturity, clinical utility, and ethical-legal robustness.

methodsA systematic search was conducted across the PubMed, Scopus, and Google Scholar databases for the period spanning 2015 to 2025. The selection and analysis processes adhered to the PRISMA 2020 guidelines. The final synthesis included 44 publications, among which 14 were empirical studies encompassing a broad spectrum of algorithmic approaches-ranging from neural networks and natural language processing (NLP) to multimodal architectures.

resultsThe review revealed a wide array of AI applications in telepsychiatry, encompassing automated diagnostics, therapeutic support, predictive modeling, and risk stratification. The most actively employed techniques include natural language and speech processing, multimodal analysis, and advanced forecasting models. However, significant barriers to implementation persist-ethical (threats to autonomy and risks of algorithmic bias), technological (limited generalizability and a lack of explainability), and legal (ambiguous accountability and weak regulatory frameworks).

conclusionsThis review underscores a growing disconnect between the rapid evolution of AI technologies and the institutional maturity of tools suitable for scalable clinical integration. Despite notable technological advances, the clinical adoption of AI in telepsychiatry remains limited. The analysis identifies persistent methodological gaps and systemic barriers that demand coordinated efforts across research, technical, and regulatory communities. It also outlines key directions for future empirical studies and interdisciplinary development of implementation standards.

Indexed as

AI ethicsartificial intelligencedigital interventionsdigital psychiatrymachine learningremote diagnosticstelepsychiatrytranslational research

Identifiers

PMID40508960
PMCPMC12155282

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.