Evidence map›Paper›PMID 42493532›Full record

ArticleNPJ digital medicine2026

Evaluating large language models for assessment of psychosis risk.

Taiyu Zhu, Alexander Tashevski, Maxime Taquet, Matilda Azis, Tia Jani, Matthew R Broome, Thomas Kabir, Amedeo Minichino, Graham K Murray, Matthew M Nour and 5 more

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Article in NPJ digital medicine, 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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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Taiyu ZhuDepartment of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. taiyu.zhu@kcl.ac.uk.
Alexander TashevskiDepartment of Psychiatry, University of Oxford, Oxford, UK.
Maxime TaquetDepartment of Psychiatry, University of Oxford, Oxford, UK.
Matilda AzisEarly Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Tia JaniDepartment of Psychiatry, University of Oxford, Oxford, UK.
Matthew R BroomeNIHR Oxford Health Biomedical Research Centre, Oxford, UK.
Thomas KabirDepartment of Psychiatry, University of Oxford, Oxford, UK.
Amedeo MinichinoDepartment of Psychiatry, University of Oxford, Oxford, UK.
Graham K MurrayDepartment of Psychiatry, University of Cambridge, Cambridge, UK.
Matthew M NourDepartment of Psychiatry, University of Oxford, Oxford, UK.
Ilina SinghDepartment of Psychiatry, University of Oxford, Oxford, UK.
Paolo Fusar-PoliNIHR Maudsley Biomedical Research Centre, London, UK.
Alejo Nevado-HolgadoDepartment of Psychiatry, University of Oxford, Oxford, UK.
Philip McGuireDepartment of Psychiatry, University of Oxford, Oxford, UK.
Dominic OliverDepartment of Psychiatry, University of Oxford, Oxford, UK. dominic.oliver@psych.ox.ac.uk.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Psychosis prevention relies on early detection of individuals at clinical high risk for psychosis (CHR-P). The effectiveness of the CHR-P state is constrained, in part, due to clinical assessments requiring specialist interpretation of narrative interviews, limiting scalability. Here, we evaluate whether large language models (LLMs; deep learning models trained on large text corpora to process and generate language) can extract clinically meaningful information from such interviews to support psychosis risk assessment. We assessed 11 open-weight LLMs on 678 partial PSYCHS interview transcripts from 373 participants (77.7% CHR-P). Models inferred CHR-P status and estimated severity and frequency across 15 symptom domains, benchmarked against researcher-rated scores. Larger models achieved the strongest classification performance (Llama-3.3-70B: accuracy = 0.80, sensitivity = 0.93, specificity = 0.58). LLM-generated symptom scores showed good correlations with researcher-rated scores (ICC

Identifiers

PMID42493532
PMCPMC13396665

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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.