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Bioengineering
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Machine Learning, Artificial Intelligence and Medicine: The Interface of Medicine,...
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Machine Learning, Artificial Intelligence and Medicine: The Interface of Medicine, Computer Science and Engineering

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 5540

Special Issue Editor

Prof. Dr. Simon W. Rabkin

E-Mail Website
Guest Editor
Department of Medicine, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: heart failure; hypertension; lipids; aortic aneurysm; cardiovascular disease and its therapy; translational medicine; AI in cardiovascular diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning has increasingly been applied in medical research. The field of medicine is data-rich, and machine learning, which utilizes computer systems to make decisions based on data, can provide invaluable insights into these data. There are many ways in which machine learning can aid in the assessment, diagnosis, and treatment of diseases, from analyzing medical records or diagnostic testing to predicting patient outcomes.

This Special Issue titled "Machine Learning, Artificial Intelligence and Medicine: The Interface of Medicine, Computer Science and Engineering" explores the potential applications and challenges of machine learning in the field of medicine. It highlights the growing interface between computer science and medicine and how the integration of these fields can provide innovative solutions to healthcare challenges.

The Special Issue covers a diverse set of topics, including the use of machine learning in disease classification, diagnosis, treatment, and patient monitoring. It also delves into issues related to the ethical implications of using machine learning in healthcare, such as data privacy and security.

Overall, the Special Issue highlights the significant potential of machine learning techniques in transforming healthcare while also acknowledging the limitations and challenges associated with their implementation. It provides an important platform for researchers and practitioners to share their expertise in this rapidly evolving field and encourage interdisciplinary collaboration to shape the future of medicine.

Prof. Dr. Simon Rabkin
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-blind 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

  • medical informatics
  • healthcare analytics
  • predictive modeling
  • digital health
  • precision medicine
  • machine learning
  • artificial intelligence

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Related Special Issues

Published Papers (4 papers)

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Research

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23 pages, 2768 KB  
Article
PSO–BiLSTM–Attention: An Interpretable Deep Learning Model Optimized by Particle Swarm Optimization for Accurate Ischemic Heart Disease Incidence Forecasting
by Ruihang Zhang, Shiyao Wang, Wei Sun and Yanming Huo
Bioengineering 2025, 12(12), 1343; https://doi.org/10.3390/bioengineering12121343 - 9 Dec 2025
Viewed by 118
Abstract
Ischemic heart disease (IHD) remains the predominant cause of global mortality, necessitating accurate incidence forecasting for effective prevention strategies. Existing statistical models inadequately capture nonlinear epidemiological patterns, while deep learning approaches lack clinical interpretability. We constructed an interpretable predictive framework combining particle swarm [...] Read more.
Ischemic heart disease (IHD) remains the predominant cause of global mortality, necessitating accurate incidence forecasting for effective prevention strategies. Existing statistical models inadequately capture nonlinear epidemiological patterns, while deep learning approaches lack clinical interpretability. We constructed an interpretable predictive framework combining particle swarm optimization (PSO), bidirectional long short-term memory (BiLSTM) networks, and a novel multi-scale attention mechanism. Age-standardized incidence rates (ASIRs) from the Global Burden of Disease (GBD) 2021 database (1990–2021) were stratified across 24 sex-age subgroups and processed through 10-year sliding windows with advanced feature engineering. SHapley Additive exPlanations (SHAP) provided a three-level interpretability analysis (global, local, and component). The framework achieved superior performance metrics: mean absolute error (MAE) of 0.0164, root mean squared error (RMSE) of 0.0206, and R2 of 0.97, demonstrating a 93.96% MAE reduction compared to ARIMA models and a 75.99% improvement over CNN–BiLSTM architectures. SHAP analysis identified females aged 60–64 years and males aged 85–89 years as primary predictive contributors. Architectural analysis revealed the residual connection captured 71.0% of the predictive contribution (main trends), while the BiLSTM–Attention pathway captured 29.0% (complex nonlinear patterns). This interpretable framework transforms opaque algorithms into transparent systems, providing precise epidemiological evidence for public health policy, resource allocation, and targeted intervention strategies for high-risk populations. Full article
(This article belongs to the Special Issue Machine Learning, Artificial Intelligence and Medicine: The Interface of Medicine, Computer Science and Engineering)
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17 pages, 920 KB  
Article
Enhancing Early GI Disease Detection with Spectral Visualization and Deep Learning
by Tsung-Jung Tsai, Kun-Hua Lee, Chu-Kuang Chou, Riya Karmakar, Arvind Mukundan, Tsung-Hsien Chen, Devansh Gupta, Gargi Ghosh, Tao-Yuan Liu and Hsiang-Chen Wang
Bioengineering 2025, 12(8), 828; https://doi.org/10.3390/bioengineering12080828 - 30 Jul 2025
Cited by 2 | Viewed by 1224
Abstract
Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision [...] Read more.
Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.056 and structural similarity index (SSIM) exceeding 90%. Trained and validated on the Kvasir v2 dataset (n = 6490) using deep learning models like ResNet-50, ResNet-101, EfficientNet-B2, both EfficientNet-B5 and EfficientNetV2-B0 were used to assess diagnostic performance across six key GI conditions. Results demonstrated that SAVE enhanced imagery and consistently outperformed raw WLI across precision, recall, and F1-score metrics, with EfficientNet-B2 and EfficientNetV2-B0 achieving the highest classification accuracy. Notably, this performance gain was achieved without the need for specialized imaging hardware. These findings highlight SAVE as a transformative solution for augmenting GI diagnostics, with the potential to significantly improve early detection, streamline clinical workflows, and broaden access to advanced imaging especially in resource constrained settings. Full article
(This article belongs to the Special Issue Machine Learning, Artificial Intelligence and Medicine: The Interface of Medicine, Computer Science and Engineering)
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Review

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72 pages, 5308 KB  
Review
Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases
by Alessia Nicosia, Nunzio Cancilla, José David Martín Guerrero, Ilenia Tinnirello and Andrea Cipollina
Bioengineering 2025, 12(10), 1069; https://doi.org/10.3390/bioengineering12101069 - 1 Oct 2025
Viewed by 2723
Abstract
Artificial Intelligence (AI) is transforming the healthcare field, offering innovative tools for improving the prediction, detection, and management of diseases. In nephrology, AI holds the potential to improve the diagnosis and treatment of kidney diseases, as well as the optimization of renal replacement [...] Read more.
Artificial Intelligence (AI) is transforming the healthcare field, offering innovative tools for improving the prediction, detection, and management of diseases. In nephrology, AI holds the potential to improve the diagnosis and treatment of kidney diseases, as well as the optimization of renal replacement therapies. In this review, a comprehensive analysis of recent literature works on artificial intelligence applied to nephrology is presented. Two key research areas structure this review. The first section examines AI models used to support early prediction of acute and chronic kidney disease. The second section explores artificial intelligence applications for hemodialytic therapies in renal insufficiency. Most studies reported high accuracy (e.g., accuracy ≥ 90%) in early prediction of kidney diseases, while fewer addressed therapy optimization and complication prevention, typically reporting moderate-to-high performance (e.g., accuracy ≃ 85%). Filling this gap and developing more accessible AI solutions that address all stages of kidney disease would therefore be crucial to support physicians’ decision-making and improve patient care. Full article
(This article belongs to the Special Issue Machine Learning, Artificial Intelligence and Medicine: The Interface of Medicine, Computer Science and Engineering)
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Other

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32 pages, 1577 KB  
Systematic Review
Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews
by Theofilos Andreadis, Antonios Gasteratos, Ioannis Seimenis and Dimitrios Koulouriotis
Bioengineering 2025, 12(11), 1160; https://doi.org/10.3390/bioengineering12111160 - 27 Oct 2025
Viewed by 1025
Abstract
Breast cancer is the second-leading cause of mortality among women worldwide. However, early detection and diagnosis significantly improve treatment outcomes. In recent years, Computer-Aided Diagnosis (CAD) systems, which leverage Artificial Intelligence (AI) techniques, have emerged as valuable tools for assisting radiologists in the [...] Read more.
Breast cancer is the second-leading cause of mortality among women worldwide. However, early detection and diagnosis significantly improve treatment outcomes. In recent years, Computer-Aided Diagnosis (CAD) systems, which leverage Artificial Intelligence (AI) techniques, have emerged as valuable tools for assisting radiologists in the accurate and efficient analysis of medical images. Following the PRISMA guidelines, this study presents the first meta-review that synthesizes evidence from 48 systematic reviews published between 2015 and January 2025. In contrast to previous reviews, which often focus on a single imaging modality or clinical task, our work provides a comprehensive overview of imaging techniques, publicly available datasets, AI methods, and clinical tasks employed in CAD systems for breast cancer diagnosis and treatment. Our analysis shows that mammography is the most frequently applied imaging modality, while DDSM, MIAS, and INBreast are the most commonly used datasets. Among clinical tasks, the detection and classification of breast lesions are the most extensively studied, with deep learning approaches being increasingly prevalent. However, current CAD systems face notable limitations, including the lack of large and diverse datasets, limited transparency and interpretability of AI-based decisions, and restricted clinical integration. By highlighting both the achievements and the limitations, this systematic review aims to support medical professionals and technical researchers in understanding the current state of CAD systems in breast cancer care and to provide guidance for future research directions. Full article
(This article belongs to the Special Issue Machine Learning, Artificial Intelligence and Medicine: The Interface of Medicine, Computer Science and Engineering)
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