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Bioengineering
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AI for Healthcare
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AI for Healthcare

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 495

Editor

Dr. Faisal Saeed

E-Mail Website
Guest Editor
Department of Computer Science, Birmingham City University, Birmingham B4 7BD, UK
Interests: artificial intelligence; data science; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on artificial intelligence (AI) for healthcare will explore the transformative potential of AI technologies in improving healthcare delivery, diagnostics, and patient outcomes. This issue will highlight advancements in machine learning, deep learning, natural language processing, and data analytics, demonstrating how these approaches can enhance clinical decision-making, enable early disease detection, and support personalized treatment strategies.

Contributions within this issue will address a wide range of applications, including medical imaging analysis, predictive modelling for disease progression, electronic health record mining, and intelligent healthcare systems. It will also focus on real-world deployment challenges, such as data quality, healthcare data security, model interpretability, and ethical considerations, ensuring that AI solutions are not only innovative but also safe, transparent, and trustworthy.

Dr. Faisal Saeed
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering 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 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

  • deep learning in healthcare
  • clinical decision support systems
  • medical imaging analysis
  • predictive analytics
  • personalized medicine
  • explainable AI (XAI)
  • healthcare
  • data analytics
  • healthcare data security
  • digital health
  • ethical AI in healthcare

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Published Papers (1 paper)

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Research

32 pages, 3072 KB  
Article
Patient-Specific Spatio-Temporal False Data Injection Attack Detection for IoMT Using a Graph-GRU Digital Twin and Kalman Innovation Features
by Eman H. Alkhammash, Fuad A. Ghaleb, Faisal Saeed and Sultan Noman Qasem
Bioengineering 2026, 13(8), 920; https://doi.org/10.3390/bioengineering13080920 - 14 Aug 2026
Abstract
The Internet of Medical Things (IoMT) is a promising technology for enabling smart and efficient healthcare systems through continuous physiological monitoring, early anomaly detection, and proactive patient management. However, IoMT sensors are vulnerable to False Data Injection Attacks (FDIAs), in which adversaries can [...] Read more.
The Internet of Medical Things (IoMT) is a promising technology for enabling smart and efficient healthcare systems through continuous physiological monitoring, early anomaly detection, and proactive patient management. However, IoMT sensors are vulnerable to False Data Injection Attacks (FDIAs), in which adversaries can manipulate sensor measurements to compromise diagnostic accuracy, mislead clinical decision-making, and threaten patient safety. Existing detection approaches often rely on population-level statistical models that may not fully capture individual physiological variations or residual-based thresholds designed for relatively simple attack scenarios, limiting their ability to exploit the spatio-temporal dependencies of multi-sensor physiological streams and detect stealthy or adversarial FDIAs. This paper proposes a patient-specific FDIA detection framework based on a Graph Convolutional Network–Gated Recurrent Unit (GCN–GRU) digital twin that learns an individual patient’s normal physiological behaviour from clean baseline telemetry. The trained digital twin is integrated into a Kalman filter as the state prediction model, and the resulting standardised innovation residuals are used as detection features. To characterise stealthy attack behaviours, four complementary window-based feature groups are extracted from the innovation sequence: innovation statistics, sensor correlation drift, temporal smoothness, and uncertainty mismatch. A CNN-1D classifier is then trained to learn discriminative temporal attack patterns from these features for accurate detection. A structured attack taxonomy comprising five stealthy and adversarial FDIA scenarios is developed, where attacks are injected as smooth gradual or abrupt coordinated modifications to sensor measurements while remaining within plausible physiological ranges. Experiments conducted on the WUSTL-EHMS-2020 benchmark dataset demonstrate that the proposed framework achieves an F1-score of 94.3%, outperforming Isolation Forest and PCA Reconstruction by 34 percentage points. Furthermore, the proposed framework reduces the false alarm rate to 3.6%, compared with 35.1% and 9.2% achieved by Isolation Forest and PCA Reconstruction, respectively. These results demonstrate the effectiveness of the proposed framework for reliable detection of stealthy FDIAs in IoMT-based healthcare systems. Full article
(This article belongs to the Special Issue AI for Healthcare)
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