Overview
- Provides current research in AI-empowered applications in medicine and health care
- Presents challenges, solutions, and development guidelines
- Covers new ideas in AI-empowered medical software and systems for researchers and practitioners
Part of the book series: Learning and Analytics in Intelligent Systems (LAIS, volume 49)
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About this book
The book delves into advancements in personalized medicine, highlighting the transition from generalized treatments to tailored strategies through AI and machine learning. It first emphasizes the role of biomarkers in training predictive models and neural networks, enhancing disease diagnosis and patient management. It then explores AI-driven healthcare systems, particularly the use of microservices to improve scalability and management. Additionally, it examines regulatory challenges, the need for AI explainability, and the PINXEL framework, which defines explainability requirements using the technology acceptance model (TAM) and the diffusion of innovation theory (DOI).
Furthermore, the book evaluates the capabilities of large language models, including ChatGPT and GPT-4V, in medical applications, with a focus on diagnosis and structured assessments in general pathology. Lastly, it introduces an AI-powered system for primary care diagnosis that integrates language models, machine learning, and rule-based systems. The interactive AI assistants “Med|Primary AI assistant” and “Dermacen Analytica” leverage natural language processing, image analysis, and multi-modal AI to enhance patient interactions and provide healthcare professionals with high-accuracy, personalized diagnostic support.
By taking a holistic approach, the book underscores the integration of AI into healthcare, aiming to support medical professionals in patient diagnosis and management with precision and adaptability.
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Keywords
Table of contents (12 chapters)
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Front Matter
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Medical Software Empowered with/by Learning Algorithms and Advanced Statistical Processing
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Front Matter
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AI-Empowered Medical Software Engineering Regulation, Validation and Explainability
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Front Matter
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Empowering Medical Software with Generative Artificial Intelligence
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Front Matter
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Authors and Affiliations
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Bibliographic Information
Book Title: Artificial Intelligence-Empowered Bio-medical Applications
Book Subtitle: Challenges, Solutions and Development Guidelines
Authors: Dimitrios P. Panagoulias, George A. Tsihrintzis, Maria Virvou
Series Title: Learning and Analytics in Intelligent Systems
DOI: https://doi.org/10.1007/978-3-031-90174-4
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025
Hardcover ISBN: 978-3-031-90173-7Published: 18 June 2025
Softcover ISBN: 978-3-031-90176-8Due: 02 July 2026
eBook ISBN: 978-3-031-90174-4Published: 17 June 2025
Series ISSN: 2662-3447
Series E-ISSN: 2662-3455
Edition Number: 1
Number of Pages: XXV, 287
Number of Illustrations: 7 b/w illustrations, 113 illustrations in colour
Topics: Biomedical Engineering and Bioengineering, Data Engineering, Computational Intelligence, Artificial Intelligence