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Bioengineering
Special Issues
Deep Learning in Medical Applications: Challenges and Opportunities
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Deep Learning in Medical Applications: Challenges and Opportunities

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 4727

Special Issue Editors

Prof. Dr. Richard Bayford

E-Mail Website
Guest Editor
Department of Natural Sciences, Middlesex University, London NW4 4BT, UK
Interests: bio-models; medical signal processing; nanoparticles imaging and therapy; deep brain stimulation; tele-medical systems; novel sensors on silicon for bio-medical applications; lab-on-a-chip for bio-makers
Dr. Sikandar Ali

E-Mail Website
Guest Editor
Department of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea
Interests: computer vision; computational histopathology, generative AI; foundation models; self supervised learning
Dr. Ali Hussain

E-Mail Website
Guest Editor
Department of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea
Interests: computer vision; computational histopathology; generative AI; foundation models; self supervised learning

Special Issue Information

Dear Colleagues,

Deep learning has revolutionized the field of medical imaging and data analysis, offering unprecedented accuracy and efficiency in diagnosing and treating various diseases. However, the integration of deep learning into medical applications is not without its challenges. One of the major hurdles is the need for large, high-quality datasets from sensors to train the algorithms effectively. Additionally, ensuring the reliability and interpretability of deep learning models in a clinical setting poses a significant challenge.

Despite these obstacles, the opportunities presented by deep learning in medicine are immense. By leveraging the power of artificial intelligence, healthcare providers can unlock new insights from complex medical data, leading to more personalized and effective treatments. Deep learning algorithms can also assist in early disease detection, improve patient outcomes, and streamline healthcare processes.

In this rapidly evolving field, researchers and practitioners are working tirelessly to overcome the challenges associated with deep learning for medical applications while harnessing its vast potential to transform the way we approach healthcare. Through collaboration and innovation, the future of medicine holds great promise with deep learning at its core.

Prof. Dr. Richard Bayford
Dr. Sikandar Ali
Dr. Ali Hussain
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

  • deep learning
  • medical imaging
  • healthcare
  • artificial intelligence

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Published Papers (2 papers)

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Research

20 pages, 2924 KB  
Article
Feature Selection and Prediction of Pediatric Tuina in Attention Deficit/Hyperactivity Disorder Management: A Machine Learning Approach Based on Parent-Reported Children’s Constitution
by Shu-Cheng Chen, Guo-Tao Wu, Han Li, Xuan Zhang, Zi-Han Li, Pong-Ming Wong, Le-Fei Han, Jing Qin, Kwai-Ching Lo, Wing-Fai Yeung and Ge Ren
Bioengineering 2025, 12(10), 1012; https://doi.org/10.3390/bioengineering12101012 - 23 Sep 2025
Cited by 1 | Viewed by 483
Abstract
Background: Attention Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children. Pediatric tuina, a traditional Chinese medicine (TCM) intervention, has shown potential in managing ADHD symptoms. Integrating machine learning (ML) into pediatric tuina could refine treatment personalization, allowing for a [...] Read more.
Background: Attention Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children. Pediatric tuina, a traditional Chinese medicine (TCM) intervention, has shown potential in managing ADHD symptoms. Integrating machine learning (ML) into pediatric tuina could refine treatment personalization, allowing for a more feasible and better parent-administered use. Methods: We employed an ML-based model to analyze parent-reported constitutional features from 1005 children diagnosed with ADHD to predict individualized pediatric tuina treatments. This study focused on feature selection and the application of several ML models, including Support Vector Machines (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), and Random Forest (RF). The key task involved identifying the most relevant features for effective TCM pattern identification and diagnosis, which would guide personalized treatment strategies. Results: The ML models displayed strong predictive performance, with the MLP model achieving the highest Area Under the Curve (AUC) of 0.90 and an accuracy (ACC) of 0.74. Seven features were selected five times in cross-validation. This facilitated a more targeted and effective pediatric tuina application tailored to individual constitution. Conclusion: This study developed an ML-based approach to enhance ADHD management in children using pediatric tuina, informed by a parent-reported questionnaire. It identified seven key features for TCM pattern identification and personalized treatment strategies. MLP achieved the highest AUC and ACC. Full article
(This article belongs to the Special Issue Deep Learning in Medical Applications: Challenges and Opportunities)
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30 pages, 1251 KB  
Article
Large Language Models in Medical Image Analysis: A Systematic Survey and Future Directions
by Bushra Urooj, Muhammad Fayaz, Shafqat Ali, L. Minh Dang and Kyung Won Kim
Bioengineering 2025, 12(8), 818; https://doi.org/10.3390/bioengineering12080818 - 29 Jul 2025
Cited by 1 | Viewed by 3942
Abstract
The integration of vision and language processing into a cohesive system has already shown promise with the application of large language models (LLMs) in medical image analysis. Their capabilities encompass the generation of medical reports, disease classification, visual question answering, and segmentation, providing [...] Read more.
The integration of vision and language processing into a cohesive system has already shown promise with the application of large language models (LLMs) in medical image analysis. Their capabilities encompass the generation of medical reports, disease classification, visual question answering, and segmentation, providing yet another approach to interpreting multimodal data. This survey aims to compile all known applications of LLMs in the medical image analysis field, spotlighting their promises alongside critical challenges and future avenues. We introduce the concept of X-stage tuning which serves as a framework for LLMs fine-tuning across multiple stages: zero stage, one stage, and multi-stage, wherein each stage corresponds to task complexity and available data. The survey describes issues like sparsity of data, hallucination in outputs, privacy issues, and the requirement for dynamic knowledge updating. Alongside these, we cover prospective features including integration of LLMs with decision support systems, multimodal learning, and federated learning for privacy-preserving model training. The goal of this work is to provide structured guidance to the targeted audience, demystifying the prospects of LLMs in medical image analysis. Full article
(This article belongs to the Special Issue Deep Learning in Medical Applications: Challenges and Opportunities)
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