Artificial Intelligence in Medical Diagnostics

Erscheinungsjahr 2025
ISBN: 978-981-954337-3
Verlag: Springer

Buch, Englisch, 187 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 519 g

This book provides a comprehensive introduction to the role of artificial intelligence (AI) in medical diagnostics, specifically targeting medical professionals who are unfamiliar with digital health and AI. It also aims to bridge the gap for AI developers who wish to deepen their understanding of clinical medicine. By examining how AI can improve diagnostic accuracy, reduce human error, and facilitate personalized medicine, this book is an indispensable resource for those seeking to harness the power of AI in healthcare.

The chapters cover a range of critical topics, including the historical evolution of diagnostic techniques, ethical and legal considerations in AI diagnostics, and the potential of AI to transform clinical decision support systems. Readers will gain insights into core AI concepts such as machine learning, overfitting, and quantification, which are essential for refining diagnostic processes. The book also explores into the limitations and risks associated with AI, such as data bias and transparency issues, ensuring a well-rounded understanding of the challenges and opportunities in this field.

Designed for medical professionals and AI experts, this book fosters interdisciplinary collaboration, paving the way for a future where hybrid intelligence—combining human and artificial intelligence—leads to more accurate, efficient, and patient-centered diagnostics. Through case studies and expert contributions, readers will discover practical solutions for AI deployment and training in healthcare settings. Whether you're a clinician looking to integrate AI into your practice or an AI developer seeking to understand clinical applications, this book equips you with the knowledge and tools to navigate the evolving landscape of medical diagnostics.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Hirosawa, Takanobu

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Ethik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Medizinische Diagnose und Diagnostik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Chapter 1 Introduction to Medical Diagnosis.- Chapter 2 Historical Evolution of Diagnostic Techniques.- Chapter 3 Overview of Diagnostic Clinical Decision Support Systems.- Chapter 4 Overview of AI and Generative AI.- Chapter 5 The Potential of AI in Diagnostics.- Chapter 6 Bridging the Gap: Collaboration Between AI Developers and Health Care Professionals.- Chapter 7 Useful Concept of AI in Diagnostics.- Chapter 8 Understanding Core AI Concepts: Machine Learning, Overfitting, and Quantification.- Chapter 9 Case Studies in AI-Enhanced Diagnostics.- Chapter 10 The Limitations of AI in Diagnostics.- Chapter 11 Ethical and Regulatory Considerations in AI Diagnostics.- Chapter 12 Interdisciplinary Collaboration for AI Development.- Chapter 13 Challenges and Solutions in AI Deployment.- Chapter 14 Training and Education for AI in Health Care.- Chapter 15 The Future Direction of AI in Diagnostics.

Über Autor(innen)

Associate Professor Hirosawa began his academic journey at the esteemed University of Tokyo, where he earned a bachelor's degree in aerospace engineering. Subsequently, he pursued higher education in medicine, securing his M.D. from the University of Tsukuba. It was during this period that he developed a profound passion for clinical knowledge and began grappling with the critical challenges intrinsic to the medical domain. To further deepen his engagement with digital health, he pursued and attained a Ph.D. from Dokkyo Medical University, focusing on AI's applications in healthcare. His research has primarily centered on digital health solutions, such as AI-assisted diagnostic tools. He has authored multiple publications on the integration of AI in diagnostics and has been actively involved in peer-review processes for journals related to digital health. His work aims to bridge the gap between clinical practice and emerging technologies, with the goal of enhancing patient care through innovative AI solutions.