Symmetry/Asymmetry in Artificial Intelligence for Healthcare Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 833

Special Issue Editors


E-Mail Website
Guest Editor
Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
Interests: adversarial learning, federated learning, out-of-distribution learning

E-Mail Website
Guest Editor
Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
Interests: computer vision; machine learning; affective computing

E-Mail Website
Guest Editor
The School of Computer Engineering and Science, Shanghai University, Shanghai, China
Interests: NLP; computational intelligence; machine learning; computer vision

Special Issue Information

Dear Colleagues,

This Special issue of Symmetry, entitled "Symmetry/Asymmetry in Artificial Intelligence for Healthcare Applications", seeks to explore the dualistic roles of AI in enhancing healthcare diagnostics and treatments. This Special Issue will particularly focus on the development and application of AI in diagnosing brain tumours, detecting and treating autism, and leveraging large multimodal models for comprehensive disease diagnosis. Contributions are invited for cutting-edge research that harnesses symmetric algorithms, which learn from balanced data sets and uniform patterns, as well as asymmetric approaches that tackle irregular, uneven data typical of real-world clinical scenarios. The objective is to elucidate how AI can maintain robustness in both symmetrical and asymmetrical data environments, ultimately improving accuracy and efficacy in healthcare outcomes. This Special Issue aims to bridge the gap between theoretical AI models and practical medical applications, highlighting the potential for AI to revolutionize personalized medicine, enhance early detection, and tailor treatment strategies.

Dr. Yonggang Zhang
Dr. Hangyu Li
Prof. Dr. Hang Yu
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. Symmetry 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 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
  • healthcare
  • symmetry
  • brain tumour diagnosis
  • autism spectrum disorders
  • multimodal models for disease diagnosis
  • personalized medicine

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

26 pages, 2572 KiB  
Article
Artificial Neural Network-Based Approach for Dynamic Analysis and Modeling of Marburg Virus Epidemics for Health Care
by Noreen Mustafa, Jamshaid Ul Rahman, Umar Ishtiaq and Ioan-Lucia Popa
Symmetry 2025, 17(4), 578; https://doi.org/10.3390/sym17040578 - 10 Apr 2025
Viewed by 287
Abstract
Artificial intelligence (AI) plays a crucial role in modern healthcare by enhancing disease modeling and outbreak prediction. In this study, we develop an epidemiological model for the Marburg virus, integrating vaccination and treatment strategies while considering vaccine efficacy and treatment failure. The model [...] Read more.
Artificial intelligence (AI) plays a crucial role in modern healthcare by enhancing disease modeling and outbreak prediction. In this study, we develop an epidemiological model for the Marburg virus, integrating vaccination and treatment strategies while considering vaccine efficacy and treatment failure. The model exhibits mathematical symmetry in its equilibrium analysis, ensuring a balanced assessment of disease dynamics across human and bat reservoir populations. We compute the Marburg-free and endemic equilibrium points, derive the secondary infection threshold, and conduct sensitivity analysis using the PRCC method to identify key disease transmission parameters that are important for disease control. To validate the theory, we optimized a deep neural network (DNN) via grid search and employed it for dynamic analysis, which also validates the cutting-edge application of AI in healthcare. We also compare AI-based predictions with traditional numerical solutions for reproduction number for humans R0h>1 and R0h<1 for validation and efficacy of the AI approach. The results demonstrate the model’s stability, efficacy, and predictive power, emphasizing the synergy between AI and mathematical epidemiology. This study provides valuable insights for public health interventions and effective disease control strategies by leveraging AI-driven simulations, highlighting AI’s potential to revolutionize and enhance early detection and tailor treatment strategies. Full article
Show Figures

Figure 1

Back to TopTop