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Bioengineering
Special Issues
Bioengineering in a Generative AI World
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Bioengineering in a Generative AI World

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1156

Special Issue Editors

Prof. Dr. Yifan Zhao

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Guest Editor
Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield MK43 0AL, UK
Interests: machine learning; artificial intelligence; human factors; pattern recognition; digital twins; instrumentation, sensors and measurement science; systems engineering; through-life engineering services
Special Issues, Collections and Topics in MDPI journals
Dr. Ulas Bagci

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Guest Editor
Radiology and Biomedical Engineering Department, Northwestern University, Chicago, IL, USA
Interests: artificial intelligence; medical artificial intelligence; biomedical imaging; digital health; explainable AI; trustworthy AI; generative AI
Special Issues, Collections and Topics in MDPI journals
Dr. Yuxiang Zhou

E-Mail Website
Guest Editor
Department of Radiology, Mayo Clinic at Arizona, Phoenix, AZ 85054, USA
Interests: image; MRI; PET; artificial intelligence; safety; medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), particularly generative AI, has emerged as an important tool in healthcare. It encompasses various machine learning techniques that can create new content or predict outcomes based on existing data and have been harnessed to address complex biological problems. For instance, the design of biomolecules, drug discovery, and personalized medicine can significantly benefit from generative models that analyze vast datasets to generate novel hypotheses, streamline experimental processes, and optimize therapeutic options. The ability to simulate biological processes and predict molecular interactions opens new avenues for researchers, paving the way for more efficient and effective solutions to health-related challenges.

This Special Issue welcomes original research articles, comprehensive reviews, and case studies that illustrate the integration of generative AI in bioengineering. Topics of interest include but are not limited to AI-driven biodesign, generative modeling in synthetic biology, machine learning applications in biostatistics, and the interplay between computational tools and experimental techniques. Contributions that document successful collaborations between bioengineers and AI experts will greatly enrich this conversation and inspire future interdisciplinary efforts.

Prof. Dr. Yifan Zhao
Dr. Ulas Bagci
Dr. Yuxiang Zhou
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. 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

  • generative AI
  • bioengineering
  • machine learning
  • synthetic biology
  • drug discovery

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Published Papers (2 papers)

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Research

20 pages, 1771 KiB  
Article
An Innovative Artificial Intelligence Classification Model for Non-Ischemic Cardiomyopathy Utilizing Cardiac Biomechanics Derived from Magnetic Resonance Imaging
by Liqiang Fu, Peifang Zhang, Liuquan Cheng, Peng Zhi, Jiayu Xu, Xiaolei Liu, Yang Zhang, Ziwen Xu and Kunlun He
Bioengineering 2025, 12(6), 670; https://doi.org/10.3390/bioengineering12060670 - 19 Jun 2025
Viewed by 401
Abstract
Significant challenges persist in diagnosing non-ischemic cardiomyopathies (NICMs) owing to early morphological overlap and subtle functional changes. While cardiac magnetic resonance (CMR) offers gold-standard structural assessment, current morphology-based AI models frequently overlook key biomechanical dysfunctions like diastolic/systolic abnormalities. To address this, we propose [...] Read more.
Significant challenges persist in diagnosing non-ischemic cardiomyopathies (NICMs) owing to early morphological overlap and subtle functional changes. While cardiac magnetic resonance (CMR) offers gold-standard structural assessment, current morphology-based AI models frequently overlook key biomechanical dysfunctions like diastolic/systolic abnormalities. To address this, we propose a dual-path hybrid deep learning framework based on CNN-LSTM and MLP, integrating anatomical features from cine CMR with biomechanical markers derived from intraventricular pressure gradients (IVPGs), significantly enhancing NICM subtype classification by capturing subtle biomechanical dysfunctions overlooked by traditional morphological models. Our dual-path architecture combines a CNN-LSTM encoder for cine CMR analysis and an MLP encoder for IVPG time-series data, followed by feature fusion and dense classification layers. Trained on a multicenter dataset of 1196 patients and externally validated on 137 patients from a distinct institution, the model achieved a superior performance (internal AUC: 0.974; external AUC: 0.962), outperforming ResNet50, VGG16, and radiomics-based SVM. Ablation studies confirmed IVPGs’ significant contribution, while gradient saliency and gradient-weighted class activation mapping (Grad-CAM) visualizations proved the model pays attention to physiologically relevant cardiac regions and phases. The framework maintained robust generalizability across imaging protocols and institutions with minimal performance degradation. By synergizing biomechanical insights with deep learning, our approach offers an interpretable, data-efficient solution for early NICM detection and subtype differentiation, holding strong translational potential for clinical practice. Full article
(This article belongs to the Special Issue Bioengineering in a Generative AI World)
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15 pages, 1486 KiB  
Article
Artificial Intelligence Outperforms Physicians in General Medical Knowledge, Except in the Paediatrics Domain: A Cross-Sectional Study
by Joana Miranda, Raquel Pereira-Silva, João Guichard, Jorge Meneses, Andreia Neves Carreira and Daniela Seixas
Bioengineering 2025, 12(6), 653; https://doi.org/10.3390/bioengineering12060653 - 14 Jun 2025
Viewed by 440
Abstract
Generative artificial intelligence (genAI) shows promising results in clinical practice. This study compared a GPT-4-turbo virtual assistant with physicians from Italy, France, Spain, and Portugal on medical knowledge derived from national exams while analysing knowledge retention over time and domain-specific performance. Via a [...] Read more.
Generative artificial intelligence (genAI) shows promising results in clinical practice. This study compared a GPT-4-turbo virtual assistant with physicians from Italy, France, Spain, and Portugal on medical knowledge derived from national exams while analysing knowledge retention over time and domain-specific performance. Via a digital platform, 17,144 physicians provided 221,574 answers to 600 exam questions between December 2022 and February 2024. Physicians were stratified by years since graduation and specialty, and the assistant answered the same questions in each native language. Differences in proportions of correct answers were tested with binomial logistic regression (odds ratios, 95% CI) or Fisher’s exact test (α = 0.05). The assistant outperformed physicians in all countries (72–96% vs. 46–62%; logistic regression, p < 0.001). Physicians also trailed the assistant across most knowledge domains (p < 0.001), except paediatrics (45% vs. 52%; Fisher, p = 0.60). Accuracy declined with seniority, falling 4–10% between the youngest and oldest cohorts (logistic regression, p < 0.001). Overall, genAI exceeds practising doctors on broad medical knowledge and may help counter knowledge attrition, though paediatrics remains a domain requiring targeted refinement. Full article
(This article belongs to the Special Issue Bioengineering in a Generative AI World)
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