Application of Artificial Intelligence in Medical Image Analysis

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 954

Special Issue Editors


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Guest Editor
Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
Interests: medical image; deep learning in image

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Guest Editor
Antal Bejczy Center for Intelligent Robotics, Obuda University, Budapest, Hungary
Interests: surgical robotics; medical robot autonomy; robot safety and standardization
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Special Issue Information

Dear Colleagues,

Happy New Year 2025! As we welcome the new year, we are thrilled to celebrate the publication of a Special Issue of Technologies (ISSN: 2227-7080) focused on the ‘Application of Artificial Intelligence in Medical Image Analysis’. This issue highlights the transformative potential of AI in advancing medical imaging and healthcare, and we would like to invite you to join us in exploring this dynamic and impactful field.

In recent years, the integration of artificial intelligence (AI) in medical imaging has revolutionized diagnostic precision, therapeutic planning, and healthcare delivery. This Special Issue aims to serve as a comprehensive platform for presenting cutting-edge research and fostering collaboration across academia, industry, and clinical practice. We believe that this collection of articles will not only showcase the latest advancements but also inspire future innovations in this dynamic field.

We invite researchers, clinicians, and technologists to contribute original research papers or comprehensive review articles on topics including, but not limited to, the following:

  • AI-driven diagnostic tools and predictive models in medical imaging: the development and application of artificial intelligence, including deep learning and machine learning, to enhance diagnostic accuracy and predictive capabilities in clinical practice.・ Radiomics and AI integration: combining radiomics with AI methodologies to extract quantitative imaging features and improve disease characterization, prognosis, and treatment response prediction.
  • Automated image segmentation and medical image reconstruction: leveraging AI-driven algorithms for the precise and efficient segmentation of medical images and advanced reconstruction techniques to improve image quality and diagnostic reliability.
  • Multi-modal image analysis for enhanced diagnostic accuracy: integrating data from multiple imaging modalities (e.g., CT, MRI, US, PET) using AI to provide comprehensive insights and support clinical decision making.
  • Applications of deep learning and machine learning in medical image processing: advancements in neural network architectures and their implementation in tasks such as classification, anomaly detection, and feature extraction.
  • Real-world data (RWD) science in AI-driven medical imaging: harnessing large-scale, real-world datasets to train and validate AI models, addressing challenges like data heterogeneity and clinical applicability.
  • Ethical considerations, validation, and standardization of AI technologies: ensuring transparency, fairness, and clinical relevance in AI model development and deployment, with a focus on regulatory and ethical compliance.

The scope of this Special Issue reflects the diversity and complexity of AI applications in medical imaging, encompassing advancements in deep learning, machine learning, and AI-driven diagnostics. It addresses critical challenges such as data quality, automated image segmentation, algorithm transparency, and clinical implementation. By highlighting innovations in radiomics and AI integration, medical image reconstruction, and multi-modal image analysis, this issue aims to bridge gaps between research, technology development, and clinical practice.

Through contributions from interdisciplinary fields, we aspire to present a holistic perspective on the current state and future prospects of AI in medical imaging, inspiring transformative solutions for enhanced healthcare delivery.

We look forward to receiving your contributions and collaborating with you to make this Special Issue a landmark in advancing medical imaging and AI innovation.

Dr. Masateru Kawakubo
Prof. Dr. Tamás Haidegger
Guest Editors

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Keywords

  • deep learning
  • machine learning
  • AI-driven diagnostics
  • radiomics and AI integration
  • automated
  • image segmentation
  • medical image reconstruction
  • multi-modal image analysis

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Published Papers (1 paper)

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15 pages, 4095 KiB  
Article
AI-Generated Mnemonic Images Improve Long-Term Retention of Coronary Artery Occlusions in STEMI: A Comparative Study
by Zahraa Alomar, Meize Guo and Tyler Bland
Technologies 2025, 13(6), 217; https://doi.org/10.3390/technologies13060217 - 26 May 2025
Viewed by 317
Abstract
Medical students face significant challenges retaining complex information, such as interpreting ECGs for coronary artery occlusions, amidst demanding curricula. While artificial intelligence (AI) is increasingly used for medical image analysis, this study explored using generative AI (DALLE-3) to create mnemonic-based images to enhance [...] Read more.
Medical students face significant challenges retaining complex information, such as interpreting ECGs for coronary artery occlusions, amidst demanding curricula. While artificial intelligence (AI) is increasingly used for medical image analysis, this study explored using generative AI (DALLE-3) to create mnemonic-based images to enhance human learning and retention of medical images, in particular, electrocardiograms (ECGs). This study is among the first to investigate generative AI as a tool not for automated diagnosis but as a human-centered educational aid designed to enhance long-term retention in complex visual tasks like ECG interpretation. We conducted a comparative study with 275 first-year medical students across six campuses; an experimental group (n = 40) received a lecture supplemented with AI-generated mnemonic ECG images, while control groups (n = 235) received standard lectures with traditional ECG diagrams. Student achievement and retention were assessed by course examinations, and student preference and engagement were measured using the Situational Interest Survey for Multimedia (SIS-M). Control groups showed a significant decline in scores on the relevant exam question over time, whereas the experimental group’s scores remained stable, indicating improved long-term retention. Experimental students also reported significantly higher situational interest in the mnemonic-based images over traditional images. AI-generated mnemonic images can effectively improve long-term retention of complex ECG interpretation skills and enhance student engagement and preference, highlighting generative AI’s potential as a valuable cognitive tool in image analysis during medical education. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)
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