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Artificial Intelligence Applications in Healthcare System, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 740

Special Issue Editors


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Guest Editor
College of Arts, Business, Law, Education & IT, Victoria University, Melbourne, VIC 3001, Australia
Interests: AI; healthcare; ECG; NLP; medical imaging
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
College of Engineering and Science, Victoria University, Footscray, VIC 3011, Australia
Interests: machine learning; biomedical informatics; Internet of Things; smart technology and cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite submissions to a Special Issue of Applied Sciences entitled “Artificial Intelligence Applications in Healthcare System, 2nd Edition".

This Special Issue will explore the transformative impact of AI in healthcare. It welcomes research and insights into AI-driven clinical decision support, healthcare data analytics, medical imaging, natural language processing, telemedicine, drug discovery, ethics, robotics, and public health applications. By addressing these topics, the issue aims to advance the integration of AI into healthcare, fostering improved patient care, efficient processes, and medical breakthroughs. It invites contributions from researchers and practitioners to share their innovative work, guiding the ethical and regulatory considerations that are crucial to this evolving field. Join us in shaping the future of healthcare through the power of artificial intelligence.

Dr. Ayman Ibaida
Dr. Khandakar Ahmed
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • clinical decision support
  • healthcare data analytics
  • medical imaging
  • telemedicine
  • drug discovery
  • medical diagnosis
  • healthcare automation

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Published Papers (1 paper)

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Research

19 pages, 2584 KiB  
Article
Deep Learning-Enhanced Spectrogram Analysis for Anatomical Region Classification in Biomedical Signals
by Abdul Karim, Semin Ryu and In cheol Jeong
Appl. Sci. 2025, 15(10), 5313; https://doi.org/10.3390/app15105313 - 9 May 2025
Viewed by 615
Abstract
Accurate classification of biomedical signals is essential for advancing non-invasive diagnostic techniques and improving clinical decision-making. This study introduces a deep learning-augmented spectrogram analysis framework for classifying biomedical signals into eight anatomically distinct regions, thereby addressing a significant deficiency in automated signal interpretation. [...] Read more.
Accurate classification of biomedical signals is essential for advancing non-invasive diagnostic techniques and improving clinical decision-making. This study introduces a deep learning-augmented spectrogram analysis framework for classifying biomedical signals into eight anatomically distinct regions, thereby addressing a significant deficiency in automated signal interpretation. The proposed approach leverages a fine-tuned ResNet50 model, pre-trained on ImageNet, and adapted for a single-channel spectrogram input to ensure robust feature extraction and high classification accuracy. Spectrograms derived from palpation and percussion signals were preprocessed into grayscale images and optimized through data augmentation and hyperparameter tuning to enhance the model’s generalization. The experimental results demonstrate a classification accuracy of 93.37%, surpassing that of conventional methods and highlighting the effectiveness of deep learning in biomedical signal processing. This study bridges the gap between machine learning and clinical applications, enabling an interpretable and region-specific classification system that enhances diagnostic precision. Future work will explore cross-domain generalization, multi-modal medical data integration, and real-time deployment for clinical applications. The findings establish a significant advancement in non-invasive diagnostics, demonstrating the potential of deep learning to refine and automate biomedical signal analysis in clinical practice. Full article
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