Evidence map›Paper›PMID 41847409›Full record

ArticleFrontiers in psychiatry2026

The augmented clinician as a framework for human-AI collaboration in mental healthcare.

Qian-Nan Ruan, Shuang-Qian Hu, Zhi-Hui ShangGuan, Sun-Meng Zhou

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Qian-Nan RuanWenzhou Seventh People's Hospital, Wenzhou, China.
Shuang-Qian HuWenzhou Center for Disease Control and Prevention, Wenzhou, China.
Zhi-Hui ShangGuanWenzhou Center for Disease Control and Prevention, Wenzhou, China.
Sun-Meng ZhouThe Affiliated Kangning Hospital of Wenzhou Medical University, Zhejiang Provincial Clinical Research Center for Mental Health, Wenzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global mental health system faces an unprecedented crisis of access, with demand for care far outstripping the supply of trained professionals. Artificial Intelligence (AI) has emerged with immense promise to bridge this gap through scalable and accessible solutions. However, its rapid and often unregulated deployment introduces significant ethical perils, including the dehumanization of care, the perpetuation of societal biases, and the risk of clinical harm. This perspective argues against the pursuit of autonomous AI therapists and instead advocates for the Augmented Clinician model. This framework positions AI as a sophisticated and transparent supportive tool that enhances, rather than replaces, human clinicians. By delegating data-intensive and administrative tasks to AI, clinicians can dedicate more time to the irreplaceable human elements of therapy such as empathy, nuanced judgment, and fostering the therapeutic alliance. We propose that this collaborative human-AI synergy is the most effective and ethically sound path to harness technology's power while ensuring mental healthcare remains fundamentally human-centered.

Indexed as

artificial intelligenceaugmented clinicianethicshuman-AI collaborationmental health

Identifiers

PMID41847409
PMCPMC12989611

What OpenQuestion holds

Textmetadata
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.