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Bioengineering
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Artificial Intelligence (AI) in Bioengineering: Second Edition
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Artificial Intelligence (AI) in Bioengineering: Second Edition

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 240

Special Issue Editor

Prof. Dr. William C. Tang

E-Mail Website
Guest Editor
1. Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan
2. Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA
Interests: microfluidics; microbiomechanics; neural engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) in bioengineering is a rapidly evolving interdisciplinary domain that leverages computational models, intelligent algorithms, and biologically inspired systems to address complex challenges in healthcare and biomedical sciences. This Special Issue invites the submission of contributions that explore the integration of AI into bioengineering applications, with the aim of advancing diagnostics, therapeutics, and personalized medicine.

Topics may include, but are not limited to, the following:

  • AI-Driven Diagnostics: Machine learning models for disease detection, medical imaging analysis, and predictive analytics.
  • Personalized Medicine: AI algorithms for tailoring treatments based on genetic, phenotypic, and lifestyle data.
  • Biomedical Signal and Image Processing: Deep learning techniques for interpreting ECG, EEG, MRI, and other biomedical signals.
  • AI in Drug Discovery and Development: Computational approaches for molecular modeling, target identification, and clinical trial optimization.
  • Smart Prosthetics and Assistive Technologies: AI-enhanced control systems for adaptive and responsive prosthetic devices.
  • AI for Biomedical Data Integration: Fusion of multi-modal data (genomic, proteomic, clinical) for holistic patient modeling.
  • Ethical and Regulatory Considerations: Addressing transparency, bias, and accountability in AI systems for healthcare.
  • Human–AI Collaboration: Designing AI systems that augment clinical decision-making and support healthcare professionals.

Prof. Dr. William C. Tang
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

  • artificial intelligence
  • bioengineering
  • computational models
  • intelligent algorithms
  • biologically inspired systems
  • healthcare
  • diagnostics
  • personalized medicine
  • medical imaging analysis
  • biomedical signal processing
  • drug discovery and development
  • smart prosthetics and assistive technologies
  • clinical decision-making

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

Related Special Issue

Published Papers (1 paper)

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Research

18 pages, 3418 KB  
Article
A Brain Connectivity Approach to Detect Diffusion-Weighted Imaging Changes in Post-Traumatic Epilepsy
by Emanuele C. Amato, Claudia Giliberti, Nicola Amoroso, Kseniia Kriukova, Alfonso Monaco, Ester Pantaleo, Tommaso Maggipinto, Loredana Bellantuono, Antonio La Calamita, Roberto Bellotti, Paul M. Vespa, Dominique Duncan and Marianna La Rocca
Bioengineering 2026, 13(6), 598; https://doi.org/10.3390/bioengineering13060598 (registering DOI) - 22 May 2026
Abstract
Traumatic brain injury (TBI) is one of the leading causes of acquired epilepsy, with a significant proportion of patients developing post-traumatic epilepsy (PTE) even months or years after the initial injury. The identification of reliable imaging biomarkers able to predict epileptogenesis remains a [...] Read more.
Traumatic brain injury (TBI) is one of the leading causes of acquired epilepsy, with a significant proportion of patients developing post-traumatic epilepsy (PTE) even months or years after the initial injury. The identification of reliable imaging biomarkers able to predict epileptogenesis remains a major clinical challenge. In recent years, diffusion-weighted imaging (DWI) and structural connectome analysis have emerged as promising tools to investigate brain network alterations associated with late seizure development. Machine learning approaches may further support the detection of predictive patterns in complex neuroimaging data. The goal of this study is to perform a binary classification between seizure-free and late seizure-affected patients following TBI, with a specific focus on the identification of the anatomical regions potentially connected with late seizure development. A dataset of 59 diffusion weighted images (DWI) scans from the EpiBioS4Rx project, including 42 seizure-free and 17 late seizure-affected TBI patients, was analyzed. A Random Forest classification algorithm was applied, incorporating network feature importance based on the Gini index to investigate model’s decisions and allow a clinical interpretation. The model reported a 69% ± 0.03 accuracy for discrimination and a 73% AUC ± 0.05. Despite the limited and imbalanced nature of the dataset, and the fact that the performance does not significantly exceed chance once all data-dependent steps are taken into account, our approach allows us to achieve accurate classification results compared to the literature and to identify brain regions potentially associated with epileptogenesis. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Bioengineering: Second Edition)
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