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Computers
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Application of Artificial Intelligence and Modeling Frameworks in Health Informatics...
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Application of Artificial Intelligence and Modeling Frameworks in Health Informatics and Related Fields

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 772

Special Issue Editor

Dr. Rafiqul Chowdhury

E-Mail Website
Guest Editor
Shannon School of Business, Cape Breton University, Sydney, NS B1M 1A2, Canada
Interests: covariate-dependent Markov; marginal; conditional; joint models; big data modeling framework; longitudinal data modeling; machine learning; AI

Special Issue Information

Dear Colleagues,

There is emerging evidence of the use of Artificial Intelligence (AI) in healthcare delivery systems, particularly in the health informatics (HI) field which uses information technology, and data analytics to improve healthcare by providing personalized treatment plans, improving patient outcomes, and designing public health strategies, among others.

Recently, the health informatics field has been enriched by adopting machine learning, deep learning, and statistical learning models to improve healthcare delivery systems. In recent years, Artificial Intelligence (AI) has shown tremendous potential and applications in health informatics. This new technology can transform the HI field, providing new tools and approaches for managing and analyzing HI data to improve patient care, research, and administration. Some areas of interest for papers include precision medicine and genomics data analysis, survival analysis, cluster data modeling, telehealth implementation during pandemics, explainable AI in clinical decision support systems, cybersecurity in healthcare systems, big data analytics for population health management, wearable technology for chronic disease management, ethical considerations of patient data sharing, etc.

Dr. Rafiqul Chowdhury
Guest Editor

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. Computers is an international peer-reviewed open access monthly 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 1800 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
  • machine learning
  • deep learning
  • disease diagnosis
  • predictive analytics
  • health informatics
  • telemedicine
  • natural language processing
  • personalized medicine
  • big data and modeling frameworks
  • telehealth solutions
  • longitudinal/repeated-measures data
  • data analysis of genomic and clinical trials

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Published Papers (1 paper)

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Research

24 pages, 58563 KiB  
Article
Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures
by Weijie Zhang, Veronika Belcheva and Tatiana Ermakova
Computers 2025, 14(5), 187; https://doi.org/10.3390/computers14050187 - 12 May 2025
Viewed by 295
Abstract
Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, requiring early detection for effective treatment. Deep learning models have been widely used for automated DR classification, with Convolutional Neural Networks (CNNs) being the most established approach. Recently, Vision Transformers (ViTs) have [...] Read more.
Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, requiring early detection for effective treatment. Deep learning models have been widely used for automated DR classification, with Convolutional Neural Networks (CNNs) being the most established approach. Recently, Vision Transformers (ViTs) have shown promise, but a direct comparison of their performance and interpretability remains limited. Additionally, hybrid models that combine CNN and transformer-based architectures have not been extensively studied. This work systematically evaluates CNNs (ResNet-50), ViTs (Vision Transformer and SwinV2-Tiny), and hybrid models (Convolutional Vision Transformer, LeViT-256, and CvT-13) on DR classification using publicly available retinal image datasets. The models are assessed based on classification accuracy and interpretability, applying Grad-CAM and Attention-Rollout to analyze decision-making patterns. Results indicate that hybrid models outperform both standalone CNNs and ViTs, achieving a better balance between local feature extraction and global context awareness. The best-performing model (CvT-13) achieved a Quadratic Weighted Kappa (QWK) score of 0.84 and an AUC of 0.93 on the test set. Interpretability analysis shows that CNNs focus on fine-grained lesion details, while ViTs exhibit broader but less localized attention. These findings provide valuable insights for optimizing deep learning models in medical imaging, supporting the development of clinically viable AI-driven DR screening systems. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Modeling Frameworks in Health Informatics and Related Fields)
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