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Bioengineering
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Artificial Intelligence (AI) in Bioengineering
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Artificial Intelligence (AI) in Bioengineering

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

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 1986

Special Issue Editors

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
Dr. Nathan Leroux

E-Mail Website
Guest Editor
PGI-15, Forschungszentrum Jülich, Aachen, Germany
Interests: neuromorphic computing; human-machine interfaces; machine learning; embedded computing architectures; in-memory computing; emerging technologies

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, aiming to advance 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
Dr. Nathan Leroux
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 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 (2 papers)

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Research

16 pages, 251 KB  
Article
Benchmarking Large Language Models on the Taiwan Neurology Board Examinations (2018–2024): A Comparative Evaluation of GPT-4o, GPT-o1, DeepSeek-V3, and DeepSeek-R1
by Shih-Yi Lin, Ying-Yu Hsu, Pei-Chun Yeh, Chien-Sheng Hsu, Wu-Huei Hsu, Shih-Sheng Chang and Chia-Hung Kao
Bioengineering 2026, 13(3), 302; https://doi.org/10.3390/bioengineering13030302 - 5 Mar 2026
Viewed by 498
Abstract
Background and Purpose: Neurology requires integration of clinical reasoning, imaging interpretation, and current knowledge, making it an ideal field for evaluating large language models (LLMs). Methods: Using 1715 questions from the Taiwan Neurology Board Examination (2018–2024), we assessed four LLMs—GPT-4o, GPT-o1, DeepSeek-V3, and [...] Read more.
Background and Purpose: Neurology requires integration of clinical reasoning, imaging interpretation, and current knowledge, making it an ideal field for evaluating large language models (LLMs). Methods: Using 1715 questions from the Taiwan Neurology Board Examination (2018–2024), we assessed four LLMs—GPT-4o, GPT-o1, DeepSeek-V3, and DeepSeek-R1—across four formats: single-choice, multiple-choice, true–false, and image-based items. Results: GPT-o1 achieved the highest overall accuracy (83.86%) and demonstrated strong performance on cognitively demanding tasks (82.50% on true–false; 77.26% on image-based). DeepSeek-V3 scored lowest (65.62%) and showed the greatest variability. Statistical analyses confirmed significant inter-model differences (p < 0.01). Accuracy declined across all models in 2024, coinciding with shifts in question design. DeepSeek-R1 was further penalized by alignment-based refusals, resulting in up to 3.81% score loss. Conclusions: These results position the Taiwan Neurology Board Exam as a rigorous benchmark for LLM evaluation and underscore GPT-o1’s potential utility in neurology education and decision support. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Bioengineering)
31 pages, 5849 KB  
Article
Interpretable Machine Learning Identifies Key Inflammatory and Morphological Drivers of Intracranial Aneurysm Rupture Risk
by Epameinondas Ntzanis, Nikolaos Papandrianos, Petros Zampakis, Vasilios Panagiotopoulos, Constantinos Koutsojannis, Christina Kalogeropoulou and Elpiniki I. Papageorgiou
Bioengineering 2026, 13(2), 226; https://doi.org/10.3390/bioengineering13020226 - 15 Feb 2026
Viewed by 749
Abstract
Traditional statistical approaches identify group-level associations between biomarkers and rupture status in intracranial aneurysms (IAs) but often miss nonlinear interactions at the patient level. Methods: The authors retrospectively analyzed 35 saccular IAs in 35 patients (57.1% ruptured) from a single center (2021–2023). Demographics, [...] Read more.
Traditional statistical approaches identify group-level associations between biomarkers and rupture status in intracranial aneurysms (IAs) but often miss nonlinear interactions at the patient level. Methods: The authors retrospectively analyzed 35 saccular IAs in 35 patients (57.1% ruptured) from a single center (2021–2023). Demographics, detailed morphology (e.g., neck width, aspect ratio, VERTI, irregular shape), and multi-site inflammatory/immune markers (CRP; complement C3/C4; IgA/IgG/IgM) were included. After preprocessing (min–max scaling; one-hot encoding), five algorithms (DT, AdaBoost, GBM, XGBoost, RF) were evaluated with stratified five-fold CV and class balancing via random oversampling. The primary model (Random Forest) was tuned with Optuna and explained using global feature importance and LIME. The results showed that baseline RF achieved CV ROC-AUC 0.81 and test ROC-AUC 0.92 (test accuracy 0.857). The tuned RF (with oversampling and Optuna) yielded a mean CV accuracy of 0.85 ± 0.09 and CV ROC-AUC of 0.98 ± 0.07 while maintaining test ROC-AUC of 0.92. The average precision on the test PR curve was 0.97. The most influential predictors combined inflammatory markers (CRP, C3, C4) with morphology (neck width, irregular shape). LIME revealed consistent local patterns: low A.CRP/C.CRP and lower C3/C4 favored Not-Broken, whereas higher CRP/complement with smaller neck and irregular shape pushed toward Broken classifications. It can be concluded that an interpretable machine learning (ML) pipeline captured clinically plausible, nonlinear interactions between inflammation and aneurysm geometry. Integrating explainable ML with conventional statistics may enhance rupture risk stratification, enable patient-level rationale, and inform personalized management. These results could significantly contribute to the quality of treatment for patients with intracranial aneurysms. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Bioengineering)
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