AI/ML for Healthcare

Navigating the AI/ML Maze Responsibly, Securely, and Sustainably
1. Auflage 2025
ISBN: 978-1-032-59430-9
Verlag: Taylor & Francis Ltd

Buch, Englisch, 296 Seiten, Format (B × H): 156 mm x 234 mm

The advent of Generative AI has democratized access to AI, prompting nearly everyone in healthcare organizations - from frontline workers to business leaders – to ask pressing questions: How can I be better equipped to support AI adoption meaningfully? How do I ensure I ask the right questions? What cautions should I exercise as I think about AI/machine learning (ML) in my business process? This book aims to answer these and other such questions and to empower healthcare professionals, at all levels, by providing them knowledge across various aspects of AI/ML, enabling them (at least in part) to realize positive, lasting business value from AI and ML initiatives. This book draws upon my experience of working in healthcare AI/ML, lessons I learned while observing leaders in this space trying to make a difference, and research (for evolving topics like sustainable AI development and securing AI/ML systems).

This book provides readers with actionable insights to build responsible, secure, and sustainable AI/ML solutions in healthcare and delves into key principles for scaling AI/ML value delivery, including establishing Machine Learning Operations (MLOps) processes and launching citizen data science programs. At its heart, this book features a healthcare-specific case study that bridges the gap between theoretical knowledge and practical application, illustrating all major concepts in a real-world context.

The final chapter of this book offers a forward-looking commentary on the future of healthcare AI/ML. It explores the potential of Generative AI for healthcare and advocates leveraging lessons from past AI/ML implementations to chart a meaningful path for embracing Generative AI. Additionally, this book emphasizes the importance of adopting “reciprocal altruism” to accelerate AI/ML value realization across the healthcare industry and provides practical recommendations toward the same.

Zielgruppe

Postgraduate and Professional Practice & Development

Autoren/Hrsg.

Monga, Kapila

Fachgebiete

• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Gesundheitswirtschaft
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

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

Über Autor(innen)

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 United States, 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.