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[null,null,"Autore",null,"Hirosawa Takanobu"]
[null,null,"Titolo",null,"Artificial Intelligence in Medical Diagnostics / / by Takanobu Hirosawa"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"981-9543-38-X"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (267 pages)"]
[null,null,"Collana",null,"Medicine Series"]
[null,null,"Disciplina",null,"616.075"]
[null,null,"Soggetti",null,"Diagnosis"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Internal medicine"]
[{"$nulls":4},"Medical ethics"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Internal Medicine"]
[{"$nulls":4},"Medical Ethics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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."]
[null,null,null,"Sommario/riassunto","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 "]
[{"$nulls":4},"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."]