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Diagnostics
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The Future of Diagnostics: Exploring the Role of Artificial Intelligence...
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The Future of Diagnostics: Exploring the Role of Artificial Intelligence in Medicine

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 4885

Special Issue Editors

Prof. Dr. Shang-Ming Zhou

E-Mail Website
Guest Editor
Centre for Health Technology, University of Plymouth, Devon PL4 8AA, UK
Interests: e-health; AI for healthcare and medicine; health data science; health informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Neil Vaughan

E-Mail Website
Guest Editor
Department of Clinical and Biomedical Science, University of Exeter, Exeter EX2 5DW, UK
Interests: artificial intelligence; data science; virtual reality; health applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jiaxiang Zhang

E-Mail Website
Guest Editor
Department of Computer Science, Swansea University, Fabian Way, Swansea SA1 8EN, UK
Interests: artificial intelligence machine learning cognitive neuroscience

Special Issue Information

Dear Colleagues,

This Special Issue delves into the transformative potential of AI in modern healthcare. It showcases the latest advancements in AI technologies that are reshaping the diagnostic landscape. From enhancing image analysis in radiology to enabling precision medicine through personalized diagnostics, it examines how AI is improving accuracy, efficiency, and patient outcomes. Through case studies, research articles, and expert insights, this Special Issue offers a comprehensive view of the challenges, opportunities, and ethical considerations surrounding the integration of AI in medical diagnosis, painting a compelling picture of the future of healthcare.

Prof. Dr. Shang-Ming Zhou
Dr. Neil Vaughan
Prof. Dr. Jiaxiang Zhang
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. Diagnostics 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 2600 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

  • diagnostics
  • artificial intelligence
  • medicine
  • healthcare
  • future

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Published Papers (2 papers)

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22 pages, 1337 KiB  
Article
Convolutional Neural Network for Depression and Schizophrenia Detection
by Carlos H. Espino-Salinas, Huizilopoztli Luna-García, Alejandra Cepeda-Argüelles, Karina Trejo-Vázquez, Luis Alberto Flores-Chaires, Jaime Mercado Reyna, Carlos E. Galván-Tejada, Claudia Acra-Despradel and Klinge Orlando Villalba-Condori
Diagnostics 2025, 15(3), 319; https://doi.org/10.3390/diagnostics15030319 - 30 Jan 2025
Cited by 1 | Viewed by 1138
Abstract
Background/Objectives: This study presents a Convolutional Neural Network (CNN) approach for detecting depression and schizophrenia using motor activity patterns represented as images. Participants’ motor activity data were captured and transformed into visual representations, enabling advanced computer vision techniques for the classification of [...] Read more.
Background/Objectives: This study presents a Convolutional Neural Network (CNN) approach for detecting depression and schizophrenia using motor activity patterns represented as images. Participants’ motor activity data were captured and transformed into visual representations, enabling advanced computer vision techniques for the classification of these mental disorders. The model’s performance was evaluated using a three-fold cross-validation, achieving an average accuracy of 95%, demonstrating the effectiveness of the proposed approach in accurately identifying mental health conditions. The objective of the study is to develop a model capable of identifying different mental disorders by processing motor data using CNN in order to provide a support tool to areas specialized in the diagnosis and efficient treatment of these psychological conditions. Methods: The methodology involved segmenting and transforming motor activity data into images, followed by a CNN training and testing phase on these visual representations. This innovative approach enables the identification of subtle motor behavior patterns, potentially indicative of specific mental states, without invasive interventions or self-reporting. Results: The results suggest that CNNs can capture discriminative features in motor activity to differentiate between individuals with depression, schizophrenia, and those without mental health diagnoses. Conclusions: These findings underscore the potential of computer vision and deep neural network techniques to contribute to early, non-invasive mental health disorder diagnosis, with significant implications for developing mental health support tools. Full article
(This article belongs to the Special Issue The Future of Diagnostics: Exploring the Role of Artificial Intelligence in Medicine)
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32 pages, 4458 KiB  
Systematic Review
Schizophrenia Detection and Classification: A Systematic Review of the Last Decade
by Arghyasree Saha, Seungmin Park, Zong Woo Geem and Pawan Kumar Singh
Diagnostics 2024, 14(23), 2698; https://doi.org/10.3390/diagnostics14232698 - 29 Nov 2024
Cited by 1 | Viewed by 3125
Abstract
Background/Objectives: Artificial Intelligence (AI) in healthcare employs advanced algorithms to analyze complex and large-scale datasets, mimicking aspects of human cognition. By automating decision-making processes based on predefined thresholds, AI enhances the accuracy and reliability of healthcare data analysis, reducing the need for human [...] Read more.
Background/Objectives: Artificial Intelligence (AI) in healthcare employs advanced algorithms to analyze complex and large-scale datasets, mimicking aspects of human cognition. By automating decision-making processes based on predefined thresholds, AI enhances the accuracy and reliability of healthcare data analysis, reducing the need for human intervention. Schizophrenia (SZ), a chronic mental health disorder affecting millions globally, is characterized by symptoms such as auditory hallucinations, paranoia, and disruptions in thought, behavior, and perception. The SZ symptoms can significantly impair daily functioning, underscoring the need for advanced diagnostic tools. Methods: This systematic review has been conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines and examines peer-reviewed studies from the last decade (2015–2024) on AI applications in SZ detection as well as classification. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number: CRD42024612364. Research has been sourced from multiple databases and screened using predefined inclusion criteria. The review evaluates the use of both Machine Learning (ML) and Deep Learning (DL) methods across multiple modalities, including Electroencephalography (EEG), Structural Magnetic Resonance Imaging (sMRI), and Functional Magnetic Resonance Imaging (fMRI). The key aspects reviewed include datasets, preprocessing techniques, and AI models. Results: The review identifies significant advancements in AI methods for SZ diagnosis, particularly in the efficacy of ML and DL models for feature extraction, classification, and multi-modal data integration. It highlights state-of-the-art AI techniques and synthesizes insights into their potential to improve diagnostic outcomes. Additionally, the analysis underscores common challenges, including dataset limitations, variability in preprocessing approaches, and the need for more interpretable models. Conclusions: This study provides a comprehensive evaluation of AI-based methods in SZ prognosis, emphasizing the strengths and limitations of current approaches. By identifying unresolved gaps, it offers valuable directions for future research in the application of AI for SZ detection and diagnosis. Full article
(This article belongs to the Special Issue The Future of Diagnostics: Exploring the Role of Artificial Intelligence in Medicine)
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