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Biomimetics
Special Issues
Bio-Inspired Artificial Intelligence in Healthcare
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Bio-Inspired Artificial Intelligence in Healthcare

A special issue of Biomimetics (ISSN 2313-7673).

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 2692

Special Issue Editors

Dr. Dongxu Gao

E-Mail Website
Guest Editor
School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
Interests: computer-aided medical analysis; robotic control and design
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhaojie Ju

E-Mail Website
Guest Editor
School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
Interests: robotics; healthcare robotics; machine learning; human robot interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of artificial intelligence (AI) and biomimetics presents groundbreaking opportunities to revolutionize healthcare. By drawing inspiration from biological systems, this interdisciplinary field explores innovative solutions to address complex medical challenges. In this rapidly evolving domain, research predominantly focuses on utilizing AI to emulate adaptive mechanisms observed in nature, leading to advances in diagnostics, medical devices, and personalized treatments.

Additionally, biomimetic principles, such as those inspired by fungal networks and bionic flight dynamics, are offering new perspectives in healthcare innovation. For instance, fungal-inspired architectures are informing the development of self-healing and adaptive medical implants, while flight-inspired dynamics are influencing drone-based medical logistics. These bio-inspired solutions promise not only efficiency and resilience, but also a sustainable approach to healthcare challenges.

This Special Issue aims to bring together cutting-edge research and foster collaboration across disciplines, including AI, biomimetics, healthcare, robotics, and materials science. By integrating these diverse perspectives, we anticipate uncovering new opportunities for innovation and advancing our understanding of bio-inspired AI applications in healthcare.

We invite researchers, practitioners, and innovators to contribute original research, reviews, or case studies addressing bio-inspired AI in healthcare. Submissions may explore, but are not limited to, the following topics:

  • AI-driven diagnostics inspired by natural systems;
  • Bio-inspired materials for self-healing and adaptive medical devices;
  • Fungal architecture-inspired healthcare applications;
  • Flight-inspired robotics for medical logistics and disaster response;
  • Computational modelling of bio-inspired healthcare systems;
  • Sustainability and resource optimization in bio-inspired medical technologies.

Dr. Dongxu Gao
Prof. Dr. Zhaojie Ju
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. 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 in healthcare
  • biomimetics
  • bio-inspired medical devices
  • fungal architectures
  • adaptive learning systems
  • self-healing implants
  • medical prosthetics
  • healthcare innovation

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

Published Papers (3 papers)

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23 pages, 808 KB  
Article
ACGA a Novel Biomimetic Hybrid Optimisation Algorithm Based on a HP Protein Visualizer: An Interpretable Web-Based Tool for 3D Protein Folding Based on the Hydrophobic-Polar Model
by Ioan Sima, Daniela-Maria Cristea, Laszlo Barna Iantovics and Virginia Niculescu
Biomimetics 2025, 10(11), 763; https://doi.org/10.3390/biomimetics10110763 - 12 Nov 2025
Viewed by 386
Abstract
In this study, we used the hydrophobic-polar (HP) two-dimensional square and three-dimensional cubic lattice models for the problem of protein structure prediction (PSP). This kind of lattice reduces computational time and calculations, the conformational space from 9n to 3n−2 [...] Read more.
In this study, we used the hydrophobic-polar (HP) two-dimensional square and three-dimensional cubic lattice models for the problem of protein structure prediction (PSP). This kind of lattice reduces computational time and calculations, the conformational space from 9n to 3n−2 for the 2D square lattice and 5n−2 for the 3D cubic lattice. Even within this context, it remains challenging for genetic algorithms or other metaheuristics to identify the optimal solutions. The contributions of the paper consist of: (1) implementation of a high-performing novel genetic algorithm (GA); instead of considering only the self-avoiding walk (SAW) conformations approached in other work, we decided to allow any conformation to appear in the population at all stages of the proposed all conformations biomimetic genetic algorithm (ACGA). This increases the probability of achieving good conformations (self avoiding walk ones), with the lowest energy. In addition to classical crossover and mutation operators, (2) we introduced specific translation operators for these two operations. We have proposed and implemented an HP Protein Visualizer tool which offers interpretability, a hybrid approach in that the visualizer gives some insight to the algorithm, that analyse and optimise protein structures HP model. The program resulted based on performed research, provides a molecular modeling tool for studying protein folding using technologies such as Node.js, Express and p5js for 3D rendering, and includes optimization algorithms to simulate protein folding. Full article
(This article belongs to the Special Issue Bio-Inspired Artificial Intelligence in Healthcare)
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20 pages, 2616 KB  
Article
Biomimetic Transfer Learning-Based Complex Gastrointestinal Polyp Classification
by Daniela-Maria Cristea, Daniela Onita and Laszlo Barna Iantovics
Biomimetics 2025, 10(10), 699; https://doi.org/10.3390/biomimetics10100699 - 15 Oct 2025
Viewed by 500
Abstract
(1) Background: This research investigates the application of Artificial Intelligence (AI), particularly biomimetic convolutional neural networks (CNNs), for the automatic classification of gastrointestinal (GI) polyps in endoscopic images. The study combines AI and Transfer learning techniques to support early detection of colorectal cancer [...] Read more.
(1) Background: This research investigates the application of Artificial Intelligence (AI), particularly biomimetic convolutional neural networks (CNNs), for the automatic classification of gastrointestinal (GI) polyps in endoscopic images. The study combines AI and Transfer learning techniques to support early detection of colorectal cancer by enhancing diagnostic accuracy with pre-trained models; (2) Methods: The Kvasir dataset, comprising 4000 annotated endoscopic images across eight polyp categories, was used. Images were pre-processed via normalisation, resizing, and data augmentation. Several CNN architectures, including state-of-the-art optimized ResNet50, DenseNet121, and MobileNetV2, were trained and evaluated. Models were assessed through training, validation, and testing phases, using performance metrics such as overall accuracy, confusion matrix, precision, recall, and F1 score; (3) Results: ResNet50 achieved the highest validation accuracy at 90.5%, followed closely by DenseNet121 with 87.5% and MobileNetV2 with 86.5%. The models demonstrated good generalisation, with small differences between training and validation accuracy. The average inference time was under 0.5 s on a computer with limited resources, confirming real-time applicability. Confusion matrix analysis indicates that common errors frequently occur between visually similar classes, particularly when reviewed by less-experienced medical physicians. These errors underscore the difficulty of distinguishing subtle features in gastrointestinal imagery and highlight the value of model-assisted diagnostics; (4) Conclusions: The obtained results confirm that Deep learning-based CNN architectures, combined with Transfer learning and optimisation techniques, can classify accurately endoscopic images and support medical diagnostics. Full article
(This article belongs to the Special Issue Bio-Inspired Artificial Intelligence in Healthcare)
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13 pages, 3259 KB  
Article
FRNet V2: A Lightweight Full-Resolution Convolutional Neural Network for OCTA Vessel Segmentation
by Dongxu Gao, Liang Wang, Youtong Fang, Du Jiang and Yalin Zheng
Biomimetics 2025, 10(4), 207; https://doi.org/10.3390/biomimetics10040207 - 27 Mar 2025
Cited by 2 | Viewed by 1103
Abstract
Optical coherence tomography angiography (OCTA) is an advanced non-invasive imaging technique that can generate three-dimensional images of retinal and choroidal vessels. It is of great value in the diagnosis and monitoring of a variety of ophthalmic diseases. However, most existing methods for blood [...] Read more.
Optical coherence tomography angiography (OCTA) is an advanced non-invasive imaging technique that can generate three-dimensional images of retinal and choroidal vessels. It is of great value in the diagnosis and monitoring of a variety of ophthalmic diseases. However, most existing methods for blood vessel segmentation in OCTA images rely on an encoder–decoder architecture. This architecture typically involves a large number of parameters and leads to slower inference speeds. To address these challenges and improve segmentation efficiency, this paper proposes a lightweight full-resolution convolutional neural network named FRNet V2 for blood vessel segmentation in OCTA images. FRNet V2 combines the ConvNeXt V2 architecture with deep separable convolution and introduces a recursive mechanism. This mechanism enhances feature representation while reducing the amount of model parameters and computational complexity. In addition, we design a lightweight hybrid adaptive attention mechanism (DWAM) that further improves the segmentation accuracy of the model through the combination of channel self-attention blocks and spatial self-attention blocks. The experimental results show that on two well-known retinal image datasets (OCTA-500 and ROSSA), FRNet V2 can achieve Dice coefficients and accuracy comparable to other methods while reducing the number of parameters by more than 90%. In conclusion, FRNet V2 provides an efficient and lightweight solution for fast and accurate OCTA image blood vessel segmentation in resource-constrained environments, offering strong support for clinical applications. Full article
(This article belongs to the Special Issue Bio-Inspired Artificial Intelligence in Healthcare)
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