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Biomimetics
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Artificial Intelligence (AI) in Biomedical Engineering: 2nd Edition
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Artificial Intelligence (AI) in Biomedical Engineering: 2nd Edition

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: closed (25 March 2026) | Viewed by 11424

Special Issue Editor

Prof. Dr. Chang Seok Bang

E-Mail Website
Guest Editor
Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Republic of Korea
Interests: gastroenterology; Helicobacter pylori; gastric cancer; colon cancer; endoscopy; deep learning; machine learning; meta-analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Artificial intelligence (AI) is at the cutting edge of innovation in biomedical engineering, enhancing various domains, such as biosignal analysis, medical imaging, disease diagnosis, and treatment planning, through sophisticated data-driven approaches. By significantly improving accuracy, efficiency, and adaptability, AI contributes to the advancement of personalized healthcare and precision medicine. One of the most exciting developments is the integration of AI with biomimicry, where AI systems draw inspiration from and mimic biological processes, leading to the creation of nature-inspired technologies. Examples include bio-mimetic robotics, adaptive prosthetics, and intelligent drug delivery systems that emulate natural behaviors. This Special Issue seeks to explore the synergy between AI and biomimicry,  and we invite researchers to submit their latest studies and innovative applications that address complex medical challenges through these cutting-edge approaches.

Prof. Dr. Chang Seok Bang
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. Biomimetics 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 2200 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
  • deep learning
  • neural network
  • large language model

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Related Special Issue

Published Papers (5 papers)

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Research

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21 pages, 2800 KB  
Article
A Trustable Spine Abnormalities Classification System Using ResNet50 and VGG16 Supported by Explainable Artificial Intelligence
by Muhammad Shahrul Zaim Ahmad, Nor Azlina Ab. Aziz, Heng Siong Lim, Anith Khairunnisa Ghazali, Mubashir Ahmad, Farshid Amirabdollahian, Afif Abdul Latiff and Kamarulzaman Ab. Aziz
Biomimetics 2026, 11(3), 206; https://doi.org/10.3390/biomimetics11030206 - 12 Mar 2026
Viewed by 497
Abstract
Deep learning has been applied in various fields and has been proven to provide good results for classification tasks. However, there is limited understanding of a deep learning model’s decisions, so deep learning is commonly described as a black box. Applying deep learning [...] Read more.
Deep learning has been applied in various fields and has been proven to provide good results for classification tasks. However, there is limited understanding of a deep learning model’s decisions, so deep learning is commonly described as a black box. Applying deep learning for critical applications such as medical diagnostic process introduces trust issues. For the deep learning model to be trusted by the medical practitioners, the methods employed by the deep learning model must be seen to be aligned with the diagnostic process employed by the medical practitioners. Explainable methods such as Grad-CAM can be applied to improve the explainability of the deep learning models by providing an visual interpretation of the deep learning classification result decision process. In this study, two deep learning models, VGG16 and ResNet50 are trained using three training methods, one with randomly initialized weights, and two transfer learning methods, which are feature extraction and fine-tuning, to classify the spinal abnormalities based on X-ray images. The classification metrics results are compared and further analyses using Grad-CAM heatmaps are included. The models also evaluated using a stratified five-fold cross-validation, results revealed some disparity between the model’s accuracy and clinical relevance. The randomly initialized VGG16 obtained a classification accuracy of 93.79% but does not focus on clinically relevant regions. On the other hand, not only do the fine-tuned ResNet50 and VGG16 obtain high accuracies of 98.22% and 99.12%, but the heatmaps show that the models focus on more relevant regions. A comparison of the two models shows that the heatmaps produced by the fine-tuned ResNet50 are in more agreement with the clinical view than the fine-tuned VGG16. This study provides a useful reference for interpreting a deep learning-based classification result using explainable method particularly in spine abnormalities analysis with Grad-CAM. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering: 2nd Edition)
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27 pages, 4932 KB  
Article
Automated Facial Pain Assessment Using Dual-Attention CNN with Clinically Calibrated High-Reliability and Reproducibility Framework
by Albert Patrick Sankoh, Ali Raza, Khadija Parwez, Wesam Shishah, Ayman Alharbi, Mubeen Javed and Muhammad Bilal
Biomimetics 2026, 11(1), 51; https://doi.org/10.3390/biomimetics11010051 - 8 Jan 2026
Cited by 2 | Viewed by 863
Abstract
Accurate and quantitative pain assessment remains a major challenge in clinical medicine, especially for patients unable to verbalize discomfort. Conventional methods based on self-reports or clinician observation are subjective and inconsistent. This study introduces a novel automated facial pain assessment framework built on [...] Read more.
Accurate and quantitative pain assessment remains a major challenge in clinical medicine, especially for patients unable to verbalize discomfort. Conventional methods based on self-reports or clinician observation are subjective and inconsistent. This study introduces a novel automated facial pain assessment framework built on a dual-attention convolutional neural network (CNN) that achieves clinically calibrated, high-reliability performance and interpretability. The architecture combines multi-head spatial attention to localize pain-relevant facial regions with an enhanced channel attention block employing triple-pooling (average, max, and standard deviation) to capture discriminative intensity features. Regularization through label smoothing (α = 0.1) and AdamW optimization ensures calibrated, stable convergence. Evaluated on a clinically annotated dataset using subject-wise stratified sampling, the proposed model achieved a test accuracy of 90.19% ± 0.94%, with an average 5-fold cross-validation accuracy of 83.60% ± 1.55%. The model further attained an F1-score of 0.90 and Cohen’s κ = 0.876, with macro- and micro-AUCs of 0.991 and 0.992, respectively. The evaluation covers five pain classes (No Pain, Mid Pain, Moderate Pain, Severe Pain, and Very Pain) using subject-wise splits comprising 5840 total images and 1160 test samples. Comparative benchmarking and ablation experiments confirm each module’s contribution, while Grad-CAM visualizations highlight physiologically relevant facial regions. The results demonstrate a robust, explainable, and reproducible framework suitable for integration into real-world automated pain-monitoring systems. Inspired by biological pain perception mechanisms and human facial muscle responses, the proposed framework aligns with biomimetic sensing principles by emulating how localized facial cues contribute to pain interpretation. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering: 2nd Edition)
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Review

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16 pages, 700 KB  
Review
Artificial Intelligence in Thermal Ablation: Current Applications and Future Directions in Microwave Technologies
by Kealan Westby, Daniel Westby, Kevin McKevitt and Brian M. Moloney
Biomimetics 2025, 10(12), 818; https://doi.org/10.3390/biomimetics10120818 - 5 Dec 2025
Viewed by 1355
Abstract
Artificial intelligence (AI) is increasingly shaping interventional oncology, with growing interest in its application across thermal ablation modalities such as radiofrequency ablation (RFA), cryoablation, high-intensity focused ultrasound (HIFU), and microwave ablation (MWA). This review characterises the current landscape of AI-enhanced thermal ablation, with [...] Read more.
Artificial intelligence (AI) is increasingly shaping interventional oncology, with growing interest in its application across thermal ablation modalities such as radiofrequency ablation (RFA), cryoablation, high-intensity focused ultrasound (HIFU), and microwave ablation (MWA). This review characterises the current landscape of AI-enhanced thermal ablation, with particular emphasis on emerging opportunities within MWA technologies. We examine how AI-driven methods—convolutional neural networks, radiomics, and reinforcement learning—are being applied to optimise patient selection, automate image segmentation, predict treatment response, and support real-time procedural guidance. Comparative insights are provided across ablation modalities to contextualise the unique challenges and opportunities presented by microwave systems. Emphasis is placed on integrating AI into clinical workflows, ensuring safety, improving consistency, and advancing personalised therapy. Tables summarising AI methods and applications, a conceptual workflow figure, and a research gap analysis for MWA are included to guide future work. While existing applications remain largely investigational, the convergence of AI with advanced imaging and energy delivery holds significant promise for precision oncology. We conclude with a roadmap for research and clinical translation, highlighting the need for prospective validation, regulatory clarity, and interdisciplinary collaboration to support the adoption of AI-enabled thermal ablation into routine practice. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering: 2nd Edition)
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24 pages, 1053 KB  
Review
Machine Learning-Driven Prediction of Reactive Oxygen Species Dynamics for Assessing Nanomaterials’ Cytotoxicity
by Zuowei Ji and Ziyu Yin
Biomimetics 2025, 10(11), 718; https://doi.org/10.3390/biomimetics10110718 - 24 Oct 2025
Cited by 2 | Viewed by 1257
Abstract
Nanomaterials (NMs) possess unique physicochemical features that set them apart from bulk counterparts. Their adjustable properties provide remarkable flexibility, giving rise to a wide array of variants. However, these attributes can also trigger complex biological interactions, particularly the generation of reactive oxygen species [...] Read more.
Nanomaterials (NMs) possess unique physicochemical features that set them apart from bulk counterparts. Their adjustable properties provide remarkable flexibility, giving rise to a wide array of variants. However, these attributes can also trigger complex biological interactions, particularly the generation of reactive oxygen species (ROS), which are central to nanomaterial-induced cytotoxicity. The ambivalent nature of ROS, essential for physiological signaling yet harmful when dysregulated, can lead to substantial health consequences. The scarcity of reliable toxicity and safety data, together with the inadequacies of conventional testing methods, highlights the urgent need for more effective strategies to assess nanomaterial-related hazards and risks. Given the intricate interplay between NMs and biological systems, computational approaches, particularly machine learning (ML), have emerged as powerful tools to model ROS dynamics, predict cytotoxic outcomes, and optimize nanomaterial design. This review highlights recent advances in applying ML to predict both the generation and neutralization of ROS by diverse NMs and to identify the critical determinants underlying ROS-mediated toxicity. These insights provide new opportunities for predictive nanotoxicology and the development of safer, application-tailored NMs. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering: 2nd Edition)
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Other

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17 pages, 583 KB  
Systematic Review
Smart Ring in Clinical Medicine: A Systematic Review
by Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee and Gwang Ho Baik
Biomimetics 2025, 10(12), 819; https://doi.org/10.3390/biomimetics10120819 - 5 Dec 2025
Viewed by 5255
Abstract
Background: Smart rings enable continuous physiological monitoring through finger-worn sensors. Despite growing consumer adoption, their clinical utility beyond sleep tracking remains unclear. Objectives: To systematically review evidence for smart ring applications in clinical medicine, assess measurement accuracy, and evaluate clinical outcomes. Methods: We [...] Read more.
Background: Smart rings enable continuous physiological monitoring through finger-worn sensors. Despite growing consumer adoption, their clinical utility beyond sleep tracking remains unclear. Objectives: To systematically review evidence for smart ring applications in clinical medicine, assess measurement accuracy, and evaluate clinical outcomes. Methods: We searched PubMed/MEDLINE, Embase, Cochrane Library, and Web of Science through 31 July 2025. Two reviewers independently screened studies and extracted data. Risk of bias was assessed using ROBINS-I and RoB 2.0. Results: From 862 citations, 107 studies met inclusion criteria including approximately 100,000 participants. Studies were equally distributed between sleep (47.7%) and non-sleep applications (52.3%). Smart rings demonstrated high accuracy: heart rate r2 = 0.996, heart rate variability r2 = 0.980, and sleep detection 93–96% sensitivity. Predictive capabilities included COVID-19 detection 2.75 days pre-symptom (82% sensitivity), inflammatory bowel disease flare prediction 7 weeks early (72% accuracy), and bipolar episode detection 3–7 days early (79% sensitivity). However, 65% of studies had moderate-to-high bias risk. Limitations included small samples, proprietary algorithms (89%), poor diversity reporting (35%), and declining adherence (80% at 3 months to 43% at 12 months). Conclusion: Smart rings have evolved into clinical tools capable of early disease detection. However, algorithmic opacity, population homogeneity, and adherence challenges require attention before widespread implementation. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Biomedical Engineering: 2nd Edition)
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