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Machine Learning Approaches for Clinical Psychology and Psychiatry

Dominic Dwyer, Peter Falkai, Nikolaos Koutsouleris • Annual Review of Clinical Psychology (2018)

Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide...

OPENALEX DOI: https://doi.org/10.1146/annurev-clinpsy-032816-045037

When more is not always better: exercise dose and children’s cognitive development from an interpretable machine learning perspective

Wenbing Zhu, Weiqiang Zhang, Liming Yong • Frontiers in Psychiatry (2026)

BackgroundExercise intervention has been associated with cognitive development in children. However, current study methods have not clarified the potential nonlinear association between exercise dosage and cognitive ability, and interpretab...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpsyt.2026.1908580

The voice of depression: speech features as biomarkers for major depressive disorder

Felix Menne, Felix Dörr, Julia Schräder, Johannes Tröger et al. • BMC Psychiatry (2024)

Abstract Background Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patient...

DOAJ Open Access (DOAJ) DOI: 10.1186/s12888-024-06253-6

PERMEPSY: a multicentre, randomized, double-blind proof-of-concept trial of personalized metacognitive training for adults with psychosis — a study protocol

Maria Lamarca, Maria Lamarca, Maria Lamarca, Maria Lamarca et al. • Frontiers in Psychiatry (2026)

BackgroundWhile psychological interventions are effective at improving symptoms of psychosis, accessible, cost- and time-efficient treatments remain limited. Personalized medicine has emerged as a promising approach, tailoring interventions...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpsyt.2026.1711659

Artificial intelligence and increasing misinformation

Scott Monteith, Tasha Glenn, John Richard Geddes, Peter C. Whybrow et al. • The British Journal of Psychiatry (2023)

With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and ...

OPENALEX Open Access DOI: https://doi.org/10.1192/bjp.2023.136

Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Heather Marriott, Renata Kabiljo, Guy P Hunt, Ahmad Al Khleifat et al. • Acta Neuropathologica Communications (2023)

Abstract Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease lands...

DOAJ Open Access (DOAJ) DOI: 10.1186/s40478-023-01686-8

Machine minds: Artificial intelligence in psychiatry

Markanday Sharma, Prateek Yadav, Srikrishna P. Panda • Industrial Psychiatry Journal (2024)

Diagnostic and interventional aspects of psychiatric care can be augmented by the use of digital health technologies. Recent studies have tried to explore the use of artificial intelligence-driven technologies in screening, diagnosing, and ...

DOAJ Open Access (DOAJ) DOI: 10.4103/ipj.ipj_157_23

AI-assisted neurocognitive assessment protocol for older adults with psychiatric disorders

Diego D. Díaz-Guerra, Marena de la C. Hernández-Lugo, Yunier Broche-Pérez, Carlos Ramos-Galarza et al. • Frontiers in Psychiatry (2025)

IntroductionEvaluating neurocognitive functions and diagnosing psychiatric disorders in older adults is challenging due to the complexity of symptoms and individual differences. An innovative approach that combines the accuracy of artificia...

DOAJ Open Access (DOAJ) DOI: 10.3389/fpsyt.2024.1516065