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Artificial Intelligence in Mental Health: Advances in Predictive Modeling, Intervention...
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Artificial Intelligence in Mental Health: Advances in Predictive Modeling, Intervention Strategies and Outcome Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 625

Special Issue Editors

Prof. Dr. Posen Lee

E-Mail Website
Guest Editor
Department of Occupation Therapy, I-Shou University, No. 8, Yida Road, Jiaosu Village, Yanchao District, Kaohsiung 82445, Taiwan
Interests: rehabilitation; balance; muscle; postural balance; stance; body equilibrium; gait; gait analysis; deep learning; motion capture; ethoprop
Dr. Chin-Hsuan Liu

E-Mail Website
Guest Editor
Department of Occupational Therapy, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan
Interests: occupational therapy; mental health; mental illness; disability; rehabilitation; machine learning
Prof. Dr. Yen-Lin Chen

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: artificial intelligence; intelligent image analytics; embedded systems; intelligent vehicles; smart manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the transformative potential of artificial intelligence (AI) in addressing the complex challenges of mental health. With the increasing availability of data and advancements in AI methodologies, this field has emerged as a critical area of innovation for improving the diagnosis, treatment, and management of mental illnesses.

We welcome original research articles, reviews, and case studies that highlight advances in AI-driven predictive modeling for the early detection and risk assessment of mental health conditions, novel intervention strategies leveraging AI to deliver personalized care, and outcome analysis tools that enhance the evaluation of therapeutic efficacy and patient progress. Topics of interest include, but are not limited to, the following:

  • Development and validation of AI models for diagnosing and predicting mental health conditions.
  • Applications of machine learning, natural language processing, and computer vision in mental health assessment and monitoring.
  • AI-powered tools for designing and delivering personalized therapeutic interventions.
  • Techniques for analyzing treatment outcomes and long-term patient well-being using AI.

This Special Issue provides an interdisciplinary platform for researchers, clinicians, and technologists to share insights and innovations at the intersection of AI and mental health. By bridging cutting-edge AI methodologies with clinical practice, we aim to advance mental health care toward greater precision, accessibility, and effectiveness.

Prof. Dr. Posen Lee
Dr. Chin-Hsuan Liu
Prof. Dr. Yen-Lin Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • mental health
  • predictive modeling
  • personalized interventions
  • outcome analysis
  • machine learning
  • natural language processing
  • psychiatric diagnosis
  • therapeutic innovation

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Published Papers (1 paper)

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24 pages, 5062 KB  
Systematic Review
Common Pitfalls and Recommendations for Use of Machine Learning in Depression Severity Estimation: DAIC-WOZ Study
by Ivan Danylenko and Olgierd Unold
Appl. Sci. 2026, 16(1), 422; https://doi.org/10.3390/app16010422 (registering DOI) - 30 Dec 2025
Abstract
The DAIC-WOZ dataset is a widely used benchmark for the task of depression severity estimation from multimodal behavioral data. Yet the reliability, reproducibility, and methodological rigor of published machine learning models remain uncertain. In this systematic review, we examined all works published through [...] Read more.
The DAIC-WOZ dataset is a widely used benchmark for the task of depression severity estimation from multimodal behavioral data. Yet the reliability, reproducibility, and methodological rigor of published machine learning models remain uncertain. In this systematic review, we examined all works published through September 2025 that mention the DAIC-WOZ dataset and report mean absolute error as an evaluation metric. Our search identified 536 papers, of which 414 remained after deduplication. Following title and abstract screening, 132 records were selected for full-text review. After applying eligibility criteria, 66 papers were included in the quality assessment stage. Of these, only five met minimal reproducibility standards (such as clear data partitioning, model description, and training protocol documentation) and were included in this review. We found that published models suffer from poor documentation and methodology, and, inter alia, identified subject leakage as a critical methodological flaw. To illustrate its impact, we conducted experiments on the DAIC-WOZ dataset, comparing the performance of the model trained with and without subject leakage. Our results indicate that leakage produces significant overestimation of the validation performance; however, our evidence is limited to the audio, text, and combined modalities of the DAIC-WOZ dataset. Without leakage, the model consistently performed worse than a simple mean predictor. Aside from poor methodological rigor, we found that the predictive accuracy of the included models is poor: reported MAEs on DAIC-WOZ are of the same magnitude as the dataset’s own PHQ-8 variability, and are comparable to or larger than the variability typically observed in general population samples. We conclude with specific recommendations aimed at improving the methodology, reproducibility, and documentation of manuscripts. Code for our experiments is publicly available. Full article
(This article belongs to the Special Issue Artificial Intelligence in Mental Health: Advances in Predictive Modeling, Intervention Strategies and Outcome Analysis)
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