Harness AI-led Revolution to Deliver Smarter, Faster, and Better Healthcare.

Key Features

Comprehensive coverage of AI tools, use cases, and strategies in healthcare

Real-world examples from telemedicine, diagnostics, and EHR systems

Thoughtful exploration of AI ethics, adoption barriers, and future directions

Book Description

Artificial Intelligence is rapidly becoming indispensable in modern healthcare-reshaping how we diagnose, treat, and deliver care. Mastering AI for Healthcare offers a clear, structured journey through this transformation, helping readers understand the critical role AI plays in improving clinical outcomes and operational efficiency.

The book begins with foundational AI concepts tailored for healthcare professionals, establishing a solid base in machine learning, neural networks, and data science. It then guides readers through practical applications across the care continuum-exploring how predictive analytics can forecast disease risks, how AI powers telemedicine and remote monitoring systems, and how it enhances clinical decision-making through smarter, data-driven EHR systems. Each chapter builds with real-world examples, case studies, and implementation frameworks, offering clarity on emerging tools like computer vision for diagnostics, personalized treatment algorithms, and intelligent automation in hospital workflows.

Whether you're a healthcare professional, researcher, or policymaker, this book provides the insights you need to stay ahead in a tech-driven world. Don't get left behind-embrace the tools shaping the future of medicine and lead the change with confidence.

What you will learn

Grasp essential AI concepts and their relevance to healthcare systems

Apply predictive analytics to real-world patient and clinical scenarios

Utilize AI in telehealth, remote monitoring, and digital health platforms

Strengthen decision-making through AI-powered EHR and clinical support

Interpret AI-driven diagnostic tools and imaging technologies

Recognize ethical, legal, and operational challenges in AI integration