Isbn: 9789372020472 - ai in medicine: clinical updates & physician perspectives (3 Ergebnisse)

Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Zustand: New.

Soft cover. Zustand: New. This book, "AI in Medicine: Clinical Updates & Physician Perspectives", is the culmination of collaborative efforts from global physician leaders, AI experts, and educators committed to responsibly shaping the next frontier of healthcare. Our goal was simple yet ambitious: To present a physician-led, evidence-based, and multidisciplinary textbook that reflects real-world AI applications in medicine and prepares healthcare professionals for a digital future. From primary care to highly specialized fields like endocrinology, pediatrics, radiology neuropsychiatry, clinical pharmacology, and many more, this book provides readers with clinical updates, practical guidance, and case-based insights. Importantly, it also includes chapters on core AI principles, responsible deployment, education and training, and generative AIall written by clinicians who have been active in both AI implementation and medical practice. This textbook is also a reflection of the vision of Doctors AI, a growing movement of clinicians upskilling for the AI era. Key Features Clinician-centric and evidence-based: Written by doctors for doctors, ensuring contextual clarity in AI adoption. Comprehensive multispecialty coverage: 30 chapters spanning diagnostics, therapeutics, public health, robotics, generative AI, and more. Education and training focus: Includes exclusive content on AI literacy, curricular integration, and real-world training models like CCAIM. Global perspectives: Contributions from Indian and international physicians contextualized to LMICs and developed systems alike. Cutting-edge developments: Includes content on LLMs, multimodal AI, wearable tech, digital therapeutics, and legal updates. Practical tools and MCQs: A dedicated self-assessment section with applied clinical questions and AI interpretation examples. Responsible AI frameworks: Emphasis on fairness, explainability, traceability, and robustness in clinical AI deployment. …