Evidence map›Paper›PMID 41533367›Full record

ArticleJAMA psychiatry2026

Artificial Intelligence and the Potential Transformation of Mental Health.

Roy H Perlis

Abstract read
In one paragraph

Article in JAMA 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

1 author.

Roy H PerlisCenter for Quantitative Health, Massachusetts General Hospital, Boston.

Funding

Scalable Phenotyping to Assay Risk in Childhood after eXposures in Pregnancy: SPARC-XPUF1MH141632 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI DOYLE, ALYSA E, EDLOW, ANDREA GOLDBERG · 2025 to 2025
$7.0M
JASPer-MH: Jointly Assessed Scalable Phenotypes for Mental HealthU01MH136059 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI ALYSA E DOYLE, ROY H. Perlis · 2024 to 2026
$6.4M
NIMH NIH HHS U01 MH136059NIMH NIH HHS UF1 MH141632
6 · The paper itself

Abstract

Importance: The potential of tools using artificial intelligence (AI) to address the many challenges in delivery of mental health care has been widely discussed. However, the possible negative consequences of AI for such care have received less attention. Observations: Integrating AI with mental health care has the potential to expand access and improve quality of care. It may also contribute to improvements in diagnosis, risk stratification, and development of novel therapeutics. At the same time, availability of AI chatbots and stratification algorithms may diminish access to human-delivered care. Reliance on AI tools may have other unanticipated adverse consequences on clinical practice, including diminished human clinician skill. The probabilistic nature of many of these tools, including large language models, makes their capacity to cause harm difficult to determine. Conclusions and Relevance: The likely benefits of AI for psychiatric care delivery must be balanced against substantial risks. Strategies to mitigate this risk may require regulation to enhance transparency and systematically evaluate the impact of AI in practice, as well as clinician training to make optimal use of these emerging methods.

Indexed as

Artificial IntelligenceMental DisordersMental Health ServicesGenerative Artificial IntelligenceHumans

Identifiers

PMID41533367
PMCPMC13439730

What OpenQuestion holds

Textmetadata
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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.