AI Advancements in Healthcare: Medical Imaging and Sensing Technologies, 2nd Edition

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 3724

Special Issue Editors


E-Mail Website
Guest Editor
Department of Artificial Intelligence and Data Science, College of Artificial Intelligence Convergence, Sejong University, Seoul, Republic of Korea
Interests: medical image analysis; artificial intelligence; deep learning; abnormalities segmentation and diagnosis; biomedical image/signal processing; image synthesis; MRI motion artifacts correction
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Intelligence and Interaction Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea
Interests: medical image reconstruction; medical image synthesis; image segmentation; medical image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has recently revolutionized healthcare with advancements in medical imaging and sensing technologies. These advancements have resulted in automated, precise, and efficient diagnosis and prognosis tools, significantly improving disease detection and patient care. AI algorithms demonstrate exceptional proficiency in analyzing medical images (MRI, CT, PET, etc.) and signals (EEG, ECG, EMG) for the classification of abnormalities, as well as the detection and segmentation of suspicious regions. This improves diagnostic accuracy, expedites decision-making processes, and offers benefits across various medical specialties. Moreover, researchers are actively addressing challenges such as artifact correction, image synthesis, and multi-modality registration to enhance medical data analysis, leading to more reliable clinical decisions and treatment plans. The integration of AI with medical imaging and sensing presents vast potential. It enables early disease detection, personalized treatment plans, and enhanced monitoring of various conditions. With ongoing advancements in AI and computing capabilities, there is potential for further innovation in healthcare, enabling more precise, efficient, and patient-centric healthcare delivery.

The second edition of this Special Issue invites novel research and technical advancements in biomedical imaging and sensing technologies. Original research papers and comprehensive reviews focusing on cutting-edge methodologies are encouraged.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Biomedical imaging;
  • Biosignals;
  • Medical image analysis;
  • Abnormalities classification and detection;
  • Medical image segmentation;
  • Medical image reconstruction;
  • Medical image denoising;
  • Medical image registration;
  • AI in biomedical systems;
  • Computer-aided diagnosis systems.

Dr. Mohammed A. Al-masni
Dr. Kanghyun Ryu
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. 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

  • biomedical imaging
  • biosignals
  • medical image analysis
  • abnormalities classification and detection
  • medical image segmentation
  • medical image reconstruction
  • medical image denoising
  • medical image registration
  • AI in biomedical systems
  • computer-aided diagnosis systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

25 pages, 4182 KB  
Article
New Gait Representation Maps for Enhanced Recognition in Clinical Gait Analysis
by Nagwan Abdel Samee, Mohammed A. Al-masni, Eman N. Marzban, Abobakr Khalil Al-Shamiri, Mugahed A. Al-antari, Maali Ibrahim Alabdulhafith, Noha F. Mahmoud and Yasser M. Kadah
Bioengineering 2025, 12(10), 1130; https://doi.org/10.3390/bioengineering12101130 - 21 Oct 2025
Viewed by 780
Abstract
Gait analysis is essential in the evaluation of neuromuscular and musculoskeletal disorders; however, traditional approaches based on expert visual observation remain subjective and often lack consistency. Accurate and objective assessment of gait impairments is critical for early diagnosis, monitoring rehabilitation progress, and guiding [...] Read more.
Gait analysis is essential in the evaluation of neuromuscular and musculoskeletal disorders; however, traditional approaches based on expert visual observation remain subjective and often lack consistency. Accurate and objective assessment of gait impairments is critical for early diagnosis, monitoring rehabilitation progress, and guiding clinical decision-making. Although Gait Energy Images (GEI) have become widely used in automated, vision-based gait analysis, they are limited in capturing boundary details and time-resolved motion dynamics, both critical for robust clinical interpretation. To overcome these limitations, we introduce four novel gait representation maps: the time-coded gait boundary image (tGBI), color-coded GEI (cGEI), time-coded gait delta image (tGDI), and color-coded boundary-to-image transform (cBIT). These representations are specifically designed to embed spatial, temporal, and boundary-specific features of the gait cycle, and are constructed from binary silhouette sequences through straightforward yet effective transformations that preserve key structural and dynamic information. Experiments on the INIT GAIT dataset demonstrate that the proposed representations consistently outperform the conventional GEI across multiple machine learning models and classification tasks involving different numbers of gait impairment categories (four and six classes). These findings highlight the potential of the proposed approaches to enhance the accuracy and reliability of automated clinical gait analysis. Full article
Show Figures

Figure 1

Review

Jump to: Research

32 pages, 1030 KB  
Review
A Review of Deep Learning Approaches Based on Segment Anything Model for Medical Image Segmentation
by Dina Koishiyeva, Dinargul Mukhammejanova, Jeong Won Kang and Assel Mukasheva
Bioengineering 2025, 12(12), 1312; https://doi.org/10.3390/bioengineering12121312 - 29 Nov 2025
Viewed by 1431
Abstract
Medical image segmentation has undergone significant changes in recent years, mainly due to the development of base models. The introduction of the Segment Anything Model (SAM) represents a major shift from task-specific architectures to universal architectures. This review discusses the adaptation of SAM [...] Read more.
Medical image segmentation has undergone significant changes in recent years, mainly due to the development of base models. The introduction of the Segment Anything Model (SAM) represents a major shift from task-specific architectures to universal architectures. This review discusses the adaptation of SAM in medical visualisation, focusing on three primary domains. Firstly, multimodal fusion frameworks implement semantic alignment of heterogeneous visual methods. Secondly, volumetric extensions transition from slice-based processing to native 3D spatial reasoning with architectures such as SAM3D, ProtoSAM-3D, and VISTA3D. Thirdly, uncertainty-aware architectures integrate probabilistic calibration for clinical interpretability, as illustrated by the SAM-U and E-Bayes SAM models. A comparative analysis reveals that SAM derivatives with effective parameters achieve Dice coefficients of 81–95%, while concomitantly reducing annotation requirements by 56–73%. Future research directions include incorporating adaptive domain hints, Bayesian self-correction mechanisms, and unified volumetric frameworks to enable autonomous generalisation across diverse medical imaging contexts. Full article
Show Figures

Figure 1

24 pages, 786 KB  
Review
Deep Learning for CT Synthesis in Radiotherapy
by Yike Guo, Yi Luo, Hamed Hooshangnejad, Rui Zhang, Xue Feng, Quan Chen, Wilfred Ngwa and Kai Ding
Bioengineering 2025, 12(12), 1297; https://doi.org/10.3390/bioengineering12121297 - 25 Nov 2025
Viewed by 995
Abstract
With the rapid development of artificial intelligence (AI), various deep learning (DL) methods have been introduced into radiation oncology. Among them, the generation of synthetic Computed Tomography (sCT) images has attracted increasing attention, as it supports different clinical scenarios, from image-guided adaptive radiotherapy [...] Read more.
With the rapid development of artificial intelligence (AI), various deep learning (DL) methods have been introduced into radiation oncology. Among them, the generation of synthetic Computed Tomography (sCT) images has attracted increasing attention, as it supports different clinical scenarios, from image-guided adaptive radiotherapy (IGART) to the simulation-free workflow. This review provides a comprehensive overview of recent studies on DL-based sCT synthesis in radiotherapy from multiple imaging modalities, including Cone-Beam CT (CBCT), Magnetic Resonance Imaging (MRI), and diagnostic CT, and discusses their clinical applications in CBCT-based online adaptive radiotherapy, MRI-guided radiotherapy, and simulation-free workflows. We also examine the architectures of representative DL models such as convolutional neural networks (CNNs) and generative adversarial networks (GANs) and summarize emerging training strategies. Finally, we discuss current challenges of clinical translation of DL algorithms into clinical practice and suggest potential directions for future research. Overall, this paper highlights the potential of AI-driven sCT generation to advance treatment planning by reducing imaging burden, improving dose accuracy, and accelerating workflow efficiency, thus ultimately improving the treatment outcome of patient care. Full article
Show Figures

Graphical abstract

Back to TopTop