Evidence map›Paper›PMID 41591923›Full record

ArticleScience progress

AI-derived research domain criteria scores from medical records predict brain inflammatory markers in psychotic disorders: A cross-sectional, real-world study.

Szabolcs Kéri, Balázs Barko, Oguz Kelemen

Abstract read
In one paragraph

Article in Science progress. 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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1 · What the graph read from it

What it found

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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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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

3 authors.

Szabolcs KériSárospatak College, Sztárai Institute, University of Tokaj, Sárospatak, Hungary.ORCID 0000-0001-7638-1741
Balázs BarkoSárospatak College, Sztárai Institute, University of Tokaj, Sárospatak, Hungary.
Oguz KelemenDepartment of Behavioral Sciences, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ObjectiveRecent advances in generative artificial intelligence (AI) and dimensional approaches in psychiatry offer scalable scoring of psychopathology, yet biological validation remains challenging. This study aimed to compare AI and human performances in scoring dimensional psychopathology and its relationship with inflammatory brain markers in psychotic disorders.MethodsIn a cross-sectional, real-world, prospective study, we generated research domain criteria (RDoC) profiles using a large-language model and human ratings from admission notes of 127 consecutively selected patients with psychotic disorders. Magnetic resonance imaging (MRI) diffusion-based restricted fraction (RF) values were extracted from the amygdala, hippocampus, and neocortex as a proxy of inflammation. We assessed the agreement between AI- and human-derived scores and their predictive value for regional RF.ResultsAI and human RDoC ratings showed moderate-to-high agreement (intraclass correlation coefficients: 0.65-0.81). AI-derived, but not human-derived, negative and positive valence RDoC scores predicted amygdala and neocortical inflammation, while social and regulatory/arousal scores predicted hippocampal RF. A significant association was found between neocortical RF and regulatory/arousal scores in the AI assessment. Both AI- and human-derived cognitive scores predicted cortical RF. When the regression analyses were corrected for multiple comparisons, only the AI-derived associations remained significant: the amygdala for negative valence and the cortex for regulatory/arousal scores.ConclusionsThese results suggest a significant correspondence between AI and human RDoC ratings. AI-based dimensional phenotyping may reflect underlying neuroinflammatory processes, offering a biologically anchored tool for precision psychiatry.

Indexed as

Artificial IntelligenceBrainInflammationPsychotic DisordersAdultBiomarkersCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansMagnetic Resonance ImagingMaleMiddle AgedProspective StudiesBiomarkersartificial intelligenceinflammationmagnetic resonance imagingpsychotic disordersRDoC

Identifiers

PMID41591923
PMCPMC12847681

What Socratic holds

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LicenceCC BY-NC
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Registered trials

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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 Socratic graph.