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Informatics
Special Issues
Health Data Management in the Age of AI
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Health Data Management in the Age of AI

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: 30 May 2026 | Viewed by 552

Special Issue Editors

Prof. Dr. Brenda Scholtz

E-Mail Website
Guest Editor
Department of Computing Sciences, Nelson Mandela University, Gqeberha 6001, South Africa
Interests: process mining; enterprise systems; business analytics; data management (particularly in a health context)
Prof. Dr. Hanlie Smuts

E-Mail Website
Guest Editor
Department of Informatics, University of Pretoria, Pretoria 0002, South Africa
Interests: information systems; society 5.0; digital transformation; big data management; artificial intelligence; knowledge management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Proper management of health data in the era of artificial intelligence (AI) has the potential to enable more accurate, timely, and personalised healthcare. It can ensure the quality, privacy and security of sensitive healthcare information and facilitate the intelligent analysis of large datasets using AI technologies. This can improve decision-making, diagnosis accuracy, treatment planning, and the prevention of non-communicable diseases (NCDs), resulting in more efficient healthcare delivery, improved patient outcomes, and greater advances in medical research. However, as AI proliferates in healthcare and shapes how health data is managed, new challenges emerge, including ethical and legal concerns, privacy and consent considerations, algorithmic bias, and data quality and interoperability.

With the aim to expand the literature on health data management by exploring the opportunities and critical questions raised by AI, we invite original research articles and review papers from scholars conducting research on recent developments in data management solutions for healthcare, particularly in the era of AI.

Topics of interest include, but are not limited to, the following:

  • Smart health data monitoring and analysis;
  • AI applications for health data management;
  • Wearables and AI in health data ecosystems;
  • Smart/intelligent coaches for personal activity monitoring;
  • Digital twins and data-driven modelling in healthcare;
  • Governance, ethics, and trust in AI-based health data management;
  • Challenges and opportunities in managing health data in the age of AI;
  • Sensor-based technologies and AI for health monitoring.

Prof. Dr. Brenda Scholtz
Prof. Dr. Hanlie Smuts
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. Informatics is an international peer-reviewed open access quarterly 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.

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

Published Papers (2 papers)

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19 pages, 4399 KB  
Article
Privacy-Preserving Synthetic Mammograms: A Generative Model Approach to Privacy-Preserving Breast Imaging Datasets
by Damir Shodiev, Egor Ushakov, Arsenii Litvinov and Yury Markin
Informatics 2025, 12(4), 112; https://doi.org/10.3390/informatics12040112 (registering DOI) - 18 Oct 2025
Abstract
Background: Significant progress has been made in the field of machine learning, enabling the development of methods for automatic interpretation of medical images that provide high-quality diagnostics. However, most of these methods require access to confidential data, making them difficult to apply under [...] Read more.
Background: Significant progress has been made in the field of machine learning, enabling the development of methods for automatic interpretation of medical images that provide high-quality diagnostics. However, most of these methods require access to confidential data, making them difficult to apply under strict privacy requirements. Existing privacy-preserving approaches, such as federated learning and dataset distillation, have limitations related to data access, visual interpretability, etc. Methods: This study explores the use of generative models to create synthetic medical data that preserves the statistical properties of the original data while ensuring privacy. The research is carried out on the VinDr-Mammo dataset of digital mammography images. A conditional generative method using Latent Diffusion Models (LDMs) is proposed with conditioning on diagnostic labels and lesion information. Diagnostic utility and privacy robustness are assessed via cancer classification tasks and re-identification tasks using Siamese neural networks and membership inference. Results: The generated synthetic data achieved a Fréchet Inception Distance (FID) of 5.8, preserving diagnostic features. A model trained solely on synthetic data achieved comparable performance to one trained on real data (ROC-AUC: 0.77 vs. 0.82). Visual assessments showed that synthetic images are indistinguishable from real ones. Privacy evaluations demonstrated a low re-identification risk (e.g., mAP@R = 0.0051 on the test set), confirming the effectiveness of the privacy-preserving approach. Conclusions: The study demonstrates that privacy-preserving generative models can produce synthetic medical images with sufficient quality for diagnostic task while significantly reducing the risk of patient re-identification. This approach enables secure data sharing and model training in privacy-sensitive domains such as medical imaging. Full article
(This article belongs to the Special Issue Health Data Management in the Age of AI)
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24 pages, 1212 KB  
Article
Heart Attack Risk Prediction via Stacked Ensemble Metamodeling: A Machine Learning Framework for Real-Time Clinical Decision Support
by Brandon N. Nava-Martinez, Sahid S. Hernandez-Hernandez, Denzel A. Rodriguez-Ramirez, Jose L. Martinez-Rodriguez, Ana B. Rios-Alvarado, Alan Diaz-Manriquez, Jose R. Martinez-Angulo and Tania Y. Guerrero-Melendez
Informatics 2025, 12(4), 110; https://doi.org/10.3390/informatics12040110 - 11 Oct 2025
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Abstract
Cardiovascular diseases claim millions of lives each year, yet timely diagnosis remains a significant challenge due to the high number of patients and associated costs. Although various machine learning solutions have been proposed for this problem, most approaches rely on careful data preprocessing [...] Read more.
Cardiovascular diseases claim millions of lives each year, yet timely diagnosis remains a significant challenge due to the high number of patients and associated costs. Although various machine learning solutions have been proposed for this problem, most approaches rely on careful data preprocessing and feature engineering workflows that could benefit from more comprehensive documentation in research publications. To address this issue, this paper presents a machine learning framework for predicting heart attack risk online. Our systematic methodology integrates a unified pipeline featuring advanced data preprocessing, optimized feature selection, and an exhaustive hyperparameter search using cross-validated grid evaluation. We employ a metamodel ensemble strategy, testing and combining six traditional supervised models along with six stacking and voting ensemble models. The proposed system achieves accuracies ranging from 90.2% to 98.9% on three independent clinical datasets, outperforming current state-of-the-art methods. Additionally, it powers a deployable, lightweight web application for real-time decision support. By merging cutting-edge AI with clinical usability, this work offers a scalable solution for early intervention in cardiovascular care. Full article
(This article belongs to the Special Issue Health Data Management in the Age of AI)
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