Paper ID:250655P1
Authos: Shazia Hassan
Title: Artificial Intelligence in Diagnostic Imaging
Enhancing Patient Care Through Advanced Algorithms and Data Integration
Publisher: Algorithm_Lab.
Conference: ICAITD 2025: The Second International Conference of AI new Technology and open Discussion
Location: Pleasanter Lounge Nakano, Tokyo Japan
Date: 1-4 june 2025
Editor: Kazuo Ohzeki (Algorithm_Lab. Professor Emeritus of Shibaura Institute of Technology, Professor of Emeritus of International Professional University of Technology in Tokyo)
Citation: Proceedings: ICAITD 2025
https://doi.org/10.63211/j.p.25.645321
pp.59-64
Abstract:
This paper presents a comprehensive overview of how artificial intelligence (AI) is revolutionizing diagnostic imaging through advanced machine learning and deep learning techniques. It explores the fundamental principles behind AI innovations—including traditional methods like Support Vector Machines and Random Forests, as well as deep learning models such as convolutional neural networks and transformer-ased architectures—and their applications in detecting, classifying, and segmenting medical images. The discussion extends to the critical role of data curation, performance evaluation, and emerging strategies like transfer learning and multi-task learning in enhancing model robustness and generalizability.
In addition, the paper reviews AI applications across various imaging modalities, including radiography, CT, MRI, ultrasound, and nuclear medicine, while highlighting key clinical tasks and use cases such as automated detection, segmentation, diagnosis, and workflow optimization. Finally, it examines the technical, operational, and regulatory challenges associated with integrating AI into clinical workflows, emphasizing the need for rigorous validation, compliance with international and national standards, and transparent risk management. Together, these insights underscore AI’s transformative potential to improve diagnostic accuracy, streamline clinical decision-making, and ultimately enhance patient
outcomes.
Keywords: Artificial Intelligence; Diagnostics Imaging; Radiography; Cardiovascular Imaging; Cardiovascular Events; Clinical Workflows; Deep Learning; MRI; Imaging Biomarkers; Computed Tomography; Workflow Optimization
Category: Position paper
Review process: Two reviewers
Publication date: July 1st 2025
First submission date: May 05th 2025
Copyright :Author(Full), AlgorithmLab.(First-in-the-world publishing rights granted by the author as the Prceedings of ICAITD 2025)
Licence:Viewer can download and view this review paper, but cannot secondary distribute (redistribute) it. (It is not Creative Commons License, nor MIT Licence) In other words, "Do not distribute" is the License
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