Artificial Intelligence in Medicine: 23rd International Conference, AIME 2025, Pavia, Italy, June 23–26, 2025, Proceedings, Part I

Author:   Riccardo Bellazzi ,  José Manuel Juarez Herrero ,  Lucia Sacchi ,  Blaž Zupan
Publisher:   Springer International Publishing AG
Volume:   15734
ISBN:  

9783031958373


Pages:   518
Publication Date:   23 June 2025
Format:   Paperback
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

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Artificial Intelligence in Medicine: 23rd International Conference, AIME 2025, Pavia, Italy, June 23–26, 2025, Proceedings, Part I


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Overview

This book constitutes the refereed proceedings of the 23rd International Conference on Artificial Intelligence in Medicine, AIME 2025, which took place in Pavia, Italy, during June 23-26, 2025.  The 49 full papers and 81 short papers included in the proceedings were carefully reviewed and selected from 311 submissions. They deal with the development of theory, methods, systems, and applications of AI in biomedicine, including the application of AI approaches in biomedical informatics, healthcare organization, and molecular medicine.

Full Product Details

Author:   Riccardo Bellazzi ,  José Manuel Juarez Herrero ,  Lucia Sacchi ,  Blaž Zupan
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Volume:   15734
ISBN:  

9783031958373


ISBN 10:   3031958373
Pages:   518
Publication Date:   23 June 2025
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

.- Combining Clinical and Gene Expression Variables via Knowledge Graph Embedding for Prediction of Coronary Artery Stenosis. .- fMRI Analysis for Alzheimer's Disease Detection: Traditional vs. Deep Learning Models.

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