This book aims to empower healthcare professionals by providing them with knowledge across various aspects of AI/ML, enabling them to realize positive, lasting business value from AI and ML initiatives. The book draws upon my experience of working in healthcare AI/ML, lessons I learned while observing leaders in this space, and research.
This book aims to empower healthcare professionals by providing them with knowledge across various aspects of AI/ML, enabling them to realize positive, lasting business value from AI and ML initiatives. The book draws upon my experience of working in healthcare AI/ML, lessons I learned while observing leaders in this space, and research.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Kapila Monga is a practitioner-turned-leader in Data Science and Machine Learning for Healthcare, with over 17 years of experience designing and delivering comprehensive data and analytics solutions. Recognized among the Top 10 Most Influential Data & Analytics Leaders in the USA's Healthcare Sector for 2024 by AIM Research, Kapila has led large teams to implement AI/ML solutions that have delivered over 150x return on investment. Most recently, Kapila served as the Head of Data Science and Automation for a leading 48-hospital health system in the U.S., where she spearheaded initiatives that bridged strategic vision with technical excellence and delivered meaningful health outcomes. Passionate about bringing healthcare industry, academia and technology together, Kapila firmly believes that the key to unlock exponential AI/ML value in healthcare lies in fostering a culture of reciprocal altruism - where shared knowledge and collaboration drive collective progress.
Inhaltsangabe
Preface 1. Healthcare AI ML Essentials 2. Leading in the age of AI 3. Begin with the end in mind 4. Navigate healthcare AI ML responsibly 5. Securing AI ML systems 6. Path to Sustainable AI in Healthcare 7. Getting Ready to Scale 8. Looking ahead
Preface 1. Healthcare AI ML Essentials 2. Leading in the age of AI 3. Begin with the end in mind 4. Navigate healthcare AI ML responsibly 5. Securing AI ML systems 6. Path to Sustainable AI in Healthcare 7. Getting Ready to Scale 8. Looking ahead
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