In the era of 'Algorithmic Medicine,' the integration of Artificial Intelligence (AI) in healthcare holds immense potential to address critical challenges faced by the industry. Drawing upon the expertise and experience of the authors in medicine, data science, medical informatics, administration, and entrepreneurship, this textbook goes beyond theoretical discussions to outline practical steps for transitioning AI from the experimental phase to real-time clinical integration. Using the Translational Science methodology, each chapter of the book concisely and perspicuously addresses the key issues associated with AI implementation in healthcare. Covering technical, clinical, ethical, regulatory, and legal considerations, the authors present evidence-based solutions and frameworks to overcome these challenges. Engaging case studies and a literature review of peer-reviewed studies and official documents from reputed organizations provide a balanced perspective, bridging the gap between AI research and actual clinical practice.

* Appeals to those who want to understand how AI is being applied to improve healthcare delivery and patient outcomes. * Describes current trends in the application of AI in healthcare. * Useful to readers to initiate projects in to enable the application of AI in healthcare/medical domains. * Provides examples of AI being applied in medicine, population health, genomics, healthcare administration and delivery.

* Appeals to those who want to understand how AI is being applied to improve healthcare delivery and patient outcomes. * Describes current trends in the application of AI in healthcare. * Useful to readers to initiate projects in to enable the application of AI in healthcare/medical domains. * Provides examples of AI being applied in medicine, population health, genomics, healthcare administration and delivery.

In the era of 'Algorithmic Medicine,' the integration of Artificial Intelligence (AI) in healthcare holds immense potential to address critical challenges faced by the industry. Drawing upon the expertise and experience of the authors in medicine, data science, medical informatics, administration, and entrepreneurship, this textbook goes beyond theoretical discussions to outline practical steps for transitioning AI from the experimental phase to real-time clinical integration. Using the Translational Science methodology, each chapter of the book concisely and perspicuously addresses the key issues associated with AI implementation in healthcare. Covering technical, clinical, ethical, regulatory, and legal considerations, the authors present evidence-based solutions and frameworks to overcome these challenges. Engaging case studies and a literature review of peer-reviewed studies and official documents from reputed organizations provide a balanced perspective, bridging the gap between AI research and actual clinical practice.