Evidence map›Paper›PMID 41909216›Full record

ArticleGlobal bioethics = Problemi di bioetica2026

Ethical considerations in the clinical application of prediction tools for severe mental disorders: perspectives from child and adolescent psychiatrists.

Ivars Neiders, Jekaterina Kalēja, Ilze Mileiko, Inese Poļaka, Signe Mežinska

Abstract read
In one paragraph

Article in Global bioethics = Problemi di bioetica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Ivars NeidersInstitute of Clinical and Preventive Medicine, University of Latvia, Riga, Latvia.
Jekaterina KalējaInstitute of Clinical and Preventive Medicine, University of Latvia, Riga, Latvia.
Ilze MileikoInstitute of Clinical and Preventive Medicine, University of Latvia, Riga, Latvia.
Inese PoļakaInstitute of Clinical and Preventive Medicine, University of Latvia, Riga, Latvia.
Signe MežinskaInstitute of Clinical and Preventive Medicine, University of Latvia, Riga, Latvia.ORCID https://orcid.org/0000-0002-3190-100X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ability to predict the risk of severe mental disorders holds considerable promise for individuals at risk, potentially enabling prevention and early intervention. Although the clinical application of such predictive models in child and adolescent psychiatry remains a future prospect, it is essential to consider their social and ethical implications that their use may entail. This study explores child and adolescent psychiatrists' views on these issues through a cross-sectional online survey distributed to members of the European Society for Child and Adolescent Psychiatry. Of the 81 respondents, the majority identified the most significant benefits of using prediction tools as enabling earlier intervention by healthcare professionals (81.5%), improving the quality of care (77.8%), and helping families enhance their resilience (63%). Participants also expressed concern about potential harms, particularly violations of privacy (74.1%), discrimination (92.6%), and the lack of explainability in Artificial Intelligence algorithms (74.1%). While most participants recognise potential medical benefits in the clinical use of predictive tools, numerous concerns must be addressed before such technologies can be considered viable. These include unresolved ethical challenges, such as risks related to privacy, potential of stigmatisation and discrimination, and algorithmic opacity, as well as limitations of healthcare systems.

Indexed as

Artificial Intelligencechild and adolescent psychiatrymental health ethicsprediction modelsRisk predictionsevere mental disorders

Identifiers

PMID41909216
PMCPMC13022997

What OpenQuestion holds

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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.