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Clinical Psychology and Neuropsychology: Integrating Social Awareness and Early Intervention...
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Clinical Psychology and Neuropsychology: Integrating Social Awareness and Early Intervention in Autism Spectrum Using AI Methods

A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 386

Special Issue Editors

Dr. Evgenia Gkintoni

E-Mail Website
Guest Editor
Department of Psychiatry, University General Hospital of Patras, 26504 Patras, Greece
Interests: clinical psychology; neuropsychology; cognitive neuroscience; psychopathology; neuroeducation; psychotherapeutic processes; applied psychology
Special Issues, Collections and Topics in MDPI journals
Dr. Constantinos Halkiopoulos

E-Mail Website
Guest Editor
Department of Management Science and Technology, University of Patras, 26504 Patras, Greece
Interests: artificial intelligence; data mining; big data; expert systems; computational cognitive science; psychometrics; decision making
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This thematic issue examines the innovative integration of artificial intelligence (AI) techniques into clinical psychology and neuropsychology to improve social awareness and provide early intervention for individuals on the autism spectrum.

The main focus is on utilizing AI technologies to enhance early detection, treatment, and the development of social skills in individuals diagnosed with autism. This Special Issue aims to examine the role of artificial intelligence (AI) in the early detection and intervention of autism spectrum disorders (ASDs), focusing on developing social awareness. Additionally, it aims to explore AI applications within clinical psychology and neuropsychology designed to enhance social skills and comprehension in young individuals with ASDs, with a focus on improving their mental health. Furthermore, the integration of AI with traditional therapeutic approaches will be discussed, including an evaluation of the benefits, challenges, ethical implications, and impact on treatment outcomes. Finally, empirical studies and case reports will be presented to demonstrate the effectiveness of AI methods in early intervention and the enhancement of social skills for individuals on the autism spectrum.

We welcome submissions of novel research, comprehensive reviews, case studies, and practical reports that align with the thematic goals. Submissions must be grounded in empirical evidence, provide a thorough account of how AI is incorporated into clinical and neuropsychological practices, and explore the potential impact of early intervention in autism spectrum disorders (ASDs).

We look forward to receiving your contributions.

Dr. Evgenia Gkintoni
Dr. Constantinos Halkiopoulos
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Healthcare 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 2700 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

  • clinical psychology
  • neuropsychology
  • autism
  • early intervention
  • screening tools
  • cognition
  • social awareness
  • machine learning
  • artificial intelligence

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Published Papers (1 paper)

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83 pages, 3724 KiB  
Systematic Review
Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review
by Evgenia Gkintoni, Maria Panagioti, Stephanos P. Vassilopoulos, Georgios Nikolaou, Basilis Boutsinas and Apostolos Vantarakis
Healthcare 2025, 13(15), 1776; https://doi.org/10.3390/healthcare13151776 - 22 Jul 2025
Abstract
Background: This systematic review examines artificial intelligence (AI) applications in neuroimaging for autism spectrum disorder (ASD), addressing six research questions regarding biomarker optimization, modality integration, social function prediction, developmental trajectories, clinical translation challenges, and multimodal data enhancement for earlier detection and improved outcomes. [...] Read more.
Background: This systematic review examines artificial intelligence (AI) applications in neuroimaging for autism spectrum disorder (ASD), addressing six research questions regarding biomarker optimization, modality integration, social function prediction, developmental trajectories, clinical translation challenges, and multimodal data enhancement for earlier detection and improved outcomes. Methods: Following PRISMA guidelines, we conducted a comprehensive literature search across 8 databases, yielding 146 studies from an initial 1872 records. These studies were systematically analyzed to address key questions regarding AI neuroimaging approaches in ASD detection and prognosis. Results: Neuroimaging combined with AI algorithms demonstrated significant potential for early ASD detection, with electroencephalography (EEG) showing promise. Machine learning classifiers achieved high diagnostic accuracy (85–99%) using features derived from neural oscillatory patterns, connectivity measures, and signal complexity metrics. Studies of infant populations have identified the 9–12-month developmental window as critical for biomarker detection and the onset of behavioral symptoms. Multimodal approaches that integrate various imaging techniques have substantially enhanced predictive capabilities, while longitudinal analyses have shown potential for tracking developmental trajectories and treatment responses. Conclusions: AI-driven neuroimaging biomarkers represent a promising frontier in ASD research, potentially enabling the detection of symptoms before they manifest behaviorally and providing objective measures of intervention efficacy. While technical and methodological challenges remain, advancements in standardization, diverse sampling, and clinical validation could facilitate the translation of findings into practice, ultimately supporting earlier intervention during critical developmental periods and improving outcomes for individuals with ASD. Future research should prioritize large-scale validation studies and standardized protocols to realize the full potential of precision medicine in ASD. Full article
(This article belongs to the Special Issue Clinical Psychology and Neuropsychology: Integrating Social Awareness and Early Intervention in Autism Spectrum Using AI Methods)
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