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Bioengineering
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Artificial Intelligence in Biomedical Engineering Applications Towards Clinical Translation
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Artificial Intelligence in Biomedical Engineering Applications Towards Clinical Translation

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 3307

Special Issue Editor

Dr. Shivaram Poigai Arunachalam

E-Mail Website
Guest Editor
Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, USA
Interests: biomedical engineering; clinical imaging; artificial intelligence; digital physiology; clinical medicine; physics informed AI/ML
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is transforming bioengineering in many disciplines by enabling faster, more accurate, and scalable solutions for healthcare. We have already seen several AI algorithms accelerating the translation of bioengineering innovations from laboratory research to clinical practice by analyzing complex biological data and modeling physiological systems. Several key areas of clinical translation of bioengineering research would benefit from AI-enhanced approaches, especially in medical imaging and diagnostics, biomaterials and tissue engineering, genomics and precision medicine, wearables and digital health, and computational modeling and simulation (digital twins). Several clinical translation challenges remain due to data issues, model generalizability, ethical and regulatory barriers, and integration into clinical workflows, which need to be addressed immediately for positive outcomes. Additionally, focusing on explainable AI (XAI), multimodal AI, real-time AI and AI-driven clinical trials, is crucial to enabling faster and more reliable solutions for patients and clinical practice. There is no doubt that AI is becoming a central enabler in bioengineering, offering tools to bridge the gap between research and clinical translation.

We solicit original articles and review papers that cover any of these topics encompassing:

AI-powered bioengineering that has the potential to accelerate personalized medicine, improve patient outcomes, and revolutionize healthcare delivery.

Dr. Shivaram Poigai Arunachalam
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 (AI)
  • bioengineering
  • clinical translation
  • medical imaging
  • digital twins
  • precision medicine
  • digital health
  • explainable AI (XAI)

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Published Papers (3 papers)

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Research

15 pages, 1109 KB  
Article
A Novel Unsupervised You Only Listen Once (YOLO) Machine Learning Platform for Automatic Detection and Characterization of Prominent Bowel Sounds Towards Precision Medicine
by Gayathri Yerrapragada, Jieun Lee, Mohammad Naveed Shariff, Poonguzhali Elangovan, Keerthy Gopalakrishnan, Avneet Kaur, Divyanshi Sood, Swetha Rapolu, Jay Gohri, Gianeshwaree Alias Rachna Panjwani, Rabiah Aslam Ansari, Jahnavi Mikkilineni, Naghmeh Asadimanesh, Thangeswaran Natarajan, Jayarajasekaran Janarthanan, Shiva Sankari Karuppiah, Vivek N. Iyer, Scott A. Helgeson, Venkata S. Akshintala and Shivaram P. Arunachalam
Bioengineering 2025, 12(11), 1271; https://doi.org/10.3390/bioengineering12111271 - 19 Nov 2025
Viewed by 580
Abstract
Phonoenterography (PEG) offers a non-invasive and radiation-free technique to assess gastrointestinal activity through acoustic signal analysis. In this feasibility study, 110 high-resolution PEG recordings (44.1 kHz, 16-bit) were acquired from eight healthy individuals, yielding 6314 prominent bowel sound (PBS) segments through automated segmentation. [...] Read more.
Phonoenterography (PEG) offers a non-invasive and radiation-free technique to assess gastrointestinal activity through acoustic signal analysis. In this feasibility study, 110 high-resolution PEG recordings (44.1 kHz, 16-bit) were acquired from eight healthy individuals, yielding 6314 prominent bowel sound (PBS) segments through automated segmentation. Each event was characterized using a 279-feature acoustic profile comprising Mel-frequency cepstral coefficients (MFCCs), their first-order derivatives (Δ-MFCCs), and six global spectral parameters. After normalization and dimensionality reduction with PCA and UMAP (cosine distance, 35 neighbors, minimum distance = 0.01), five clustering strategies were evaluated. K-Means (k = 5) achieved the most favorable balance between cluster quality (silhouette = 0.60; Calinski–Harabasz = 19,165; Davies–Bouldin = 0.68) and interpretability, consistently identifying five acoustic patterns: single-burst, multiple-burst, harmonic, random-continuous, and multi-modal. Temporal modeling of clustered events further revealed distinct sequential dynamics, with Single-Burst events showing the longest dwell times, random continuous the shortest, and strong diagonal elements in the transition matrix confirming measurable state persistence. Frequent transitions between random continuous and multi-modal states suggested dynamic exchanges between transient and overlapping motility patterns. Together, these findings demonstrate that unsupervised PEG-based analysis can capture both acoustic variability and temporal organization of bowel sounds. This annotation-free approach provides a scalable framework for real-time gastrointestinal monitoring and holds potential for clinical translation in conditions such as postoperative ileus, bowel obstruction, irritable bowel syndrome, and inflammatory bowel disease. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Engineering Applications Towards Clinical Translation)
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28 pages, 15283 KB  
Article
A Study on the Interpretability of Diabetic Retinopathy Diagnostic Models
by Zerui Zhang, Hongbo Zhao, Li Dong, Lin Luo and Hao Wang
Bioengineering 2025, 12(11), 1231; https://doi.org/10.3390/bioengineering12111231 - 10 Nov 2025
Cited by 1 | Viewed by 492
Abstract
This study focuses on the interpretability of diabetic retinopathy classification models. Seven widely used interpretability methods—Gradient, SmoothGrad, Integrated Gradients, SHAP, DeepLIFT, Grad-CAM++, and ScoreCAM—are applied to assess the interpretability of four representative deep learning architectures, VGG, ResNet, DenseNet, and EfficientNet, on fundus images. [...] Read more.
This study focuses on the interpretability of diabetic retinopathy classification models. Seven widely used interpretability methods—Gradient, SmoothGrad, Integrated Gradients, SHAP, DeepLIFT, Grad-CAM++, and ScoreCAM—are applied to assess the interpretability of four representative deep learning architectures, VGG, ResNet, DenseNet, and EfficientNet, on fundus images. Through saliency map visualization, perturbation curve analysis, and trend correlation analysis, combined with four quantitative metrics—saliency map entropy, AOPC score, Recall, and Dice coefficient—the interpretability performance of the models is comprehensively assessed from both qualitative and quantitative perspectives. The results show that model architecture greatly influences interpretability quality: models with simpler structures and clearer feature extraction paths (such as VGG) perform better in terms of interpretability, while deeper or lightweight architectures exhibit certain limitations. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Engineering Applications Towards Clinical Translation)
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22 pages, 1320 KB  
Article
Comparative Evaluation of Advanced Chunking for Retrieval-Augmented Generation in Large Language Models for Clinical Decision Support
by Cesar Abraham Gomez-Cabello, Srinivasagam Prabha, Syed Ali Haider, Ariana Genovese, Bernardo G. Collaco, Nadia G. Wood, Sanjay Bagaria and Antonio Jorge Forte
Bioengineering 2025, 12(11), 1194; https://doi.org/10.3390/bioengineering12111194 - 1 Nov 2025
Viewed by 1938
Abstract
Retrieval-augmented generation (RAG) quality depends on how source documents are segmented before indexing; fixed-length chunks can split concepts or add noise, reducing precision. We evaluated whether proposition, semantic, and adaptive chunking improve accuracy and relevance for safer clinical decision support. Using a curated [...] Read more.
Retrieval-augmented generation (RAG) quality depends on how source documents are segmented before indexing; fixed-length chunks can split concepts or add noise, reducing precision. We evaluated whether proposition, semantic, and adaptive chunking improve accuracy and relevance for safer clinical decision support. Using a curated domain knowledge base with Gemini 1.0 Pro, we built four otherwise identical RAG pipelines that differed only in the chunking strategy: adaptive length, proposition, semantic, and a fixed token-dependent baseline. Thirty common postoperative rhinoplasty questions were submitted to each pipeline. Outcomes included medical accuracy and clinical relevance (3-point Likert scale) and retrieval precision, recall, and F1; group differences were tested with ANOVA and Tukey post hoc analyses. Adaptive chunking achieved the highest accuracy—87% (Likert 2.37 ± 0.72) versus baseline 50% (1.63 ± 0.72; p = 0.001)—and the highest relevance (93%, 2.90 ± 0.40). Retrieval metrics were strongest with adaptive (precision 0.50, recall 0.88, F1 0.64) versus baseline (0.17, 0.40, 0.24). Proposition and semantic strategies improved all metrics relative to baseline, though less than adaptive. Aligning chunks to logical topic boundaries yielded more accurate, relevant answers without modifying the language model, offering a model-agnostic, data-source-neutral lever to enhance the safety and utility of LLM-based clinical decision support. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Engineering Applications Towards Clinical Translation)
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