Open Access

REVIEW

The Contribution of Artificial Neural Networks to Psychological Assessment: A Scoping Review

Monica Casella¹, Raffaella Esposito^2,*, Michela Ponticorvo¹

1 Department of Humanistic Studies, University of Naples Federico II, Naples, Italy
2 ICAROS Center, University of Naples Federico II, Naples, Italy

* Corresponding Author: Raffaella Esposito. Email:

International Journal of Mental Health Promotion 2026, 28(8), 1 https://doi.org/10.32604/ijmhp.2026.079327

Received 19 January 2026; Accepted 14 July 2026; Issue published 31 August 2026

Abstract

Background: Artificial Neural Networks (ANNs) are increasingly used in psychological assessment to score measures, classify clinical presentations, monitor symptoms, estimate risk, and tailor interventions. Methods: This scoping review maps ANN applications in psychological assessment and diagnosis and examines their relevance to psychiatric practice. A structured search of Scopus and Web of Science, followed by independent screening and data charting by three reviewers, identified 31 studies published between 2020 and 2023. Results: The literature demonstrates broad methodological versatility across psychometric, behavioral, textual, image, and sensor data, but clinical readiness remains limited by heterogeneous samples, architectures, metrics, and validation procedures. Exceptionally high accuracy estimates require cautious interpretation, particularly in small or single-site datasets. Persistent barriers include limited external validation, insufficient interpretability, demographic and cultural bias, privacy and governance concerns, workflow integration, and unclear regulatory and medico-legal responsibility. Conclusions: ANNs should currently be considered clinical decision-support tools rather than substitutes for professional judgment.

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Keywords

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Supplementary Material

Supplementary Material File

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