Evidence map›Paper›PMID 42918865›Full record

ReviewDer Nervenarzt2026

[Artificial intelligence in the early detection of psychotic disorders: opportunities and limitations of clinical implementation].

Clara Weyer, Nikos Diederichs, Eva Meisenzahl, Nikolaos Koutsouleris

Abstract readEnglish AbstractReview
PubMed Publisher
In one paragraph

Review in Der Nervenarzt, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

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

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

4 authors.

Clara WeyerKlinik für Psychiatrie und Psychotherapie, LMU München, München, Deutschland. clara.weyer@med.uni-muenchen.de.ORCID https://orcid.org/0000-0001-6916-3447
Nikos DiederichsKlinik für Psychiatrie und Psychotherapie, LMU München, München, Deutschland.
Eva MeisenzahlKlinik und Poliklinik für Psychiatrie und Psychotherapie der Heinrich-Heine-Universität Düsseldorf, LVR-Klinik, Düsseldorf, Deutschland.
Nikolaos KoutsoulerisKlinik für Psychiatrie und Psychotherapie, LMU München, München, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early detection of psychotic disorders is complicated by nonspecific symptoms, heterogeneous trajectories and the limited ability of established clinical instruments to generate accurate individual-level predictions. Data-driven artificial intelligence can integrate complex clinical and biological data and achieves moderate to good predictive performance, which often declines on external validation. Despite substantial methodological advances, a marked translational gap between model development and clinical application persists, mainly due to limited generalizability as well as practical, ethical and regulatory requirements. Artificial intelligence can complement clinical expertise and facilitate access to appropriate psychiatric care provided that it is implemented transparently and close to real-world care, subject to health economic evaluation and developed with the active involvement of experts by experience.

Indexed as

GeneralizabilityNonspecific symptomsPredictive performancePreventionRisk stratification

Identifiers

What OpenQuestion holds

Textmetadata
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.