Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes, Second Edition

Healthcare data can be abundant; turning it into useful, responsible decisions is the harder part. In Machine Learning and AI for Healthcare, Arjun Panesar connects the fundamentals of machine learning and artificial intelligence to the realities of healthcare data, implementation, and oversight.

This second edition moves from core concepts into algorithms, practical model workflows, evaluation, ethics, and case studies—giving healthcare professionals and technically curious readers a structured way to understand where these systems fit, and what questions their use raises.

From healthcare data to machine learning

The opening chapters establish a foundation in AI, big data, and machine learning. They consider how healthcare information is collected and managed, and why governance matters when data is used to inform decisions. This context helps readers see machine learning not as an isolated technical exercise, but as part of a broader health-data environment.

Explore algorithms and the work around them

Panesar introduces machine learning algorithms alongside the steps involved in putting them to work. The book covers project design, data preparation, and model evaluation, as well as topics including neural networks, natural language processing, and deep learning. The emphasis on workflow makes room for the questions that accompany model selection: what data is available, how should performance be assessed, and how might a system be used in practice?

Consider ethics alongside implementation

Healthcare applications involve more than technical performance. Dedicated coverage of machine learning and AI ethics places governance and responsible use within the conversation, while chapters on the future of healthcare and case studies connect broader themes to examples from the field.

Who may find it useful?

Healthcare professionals looking for an introduction to machine learning applications are the book’s stated audience. Readers studying health informatics, healthcare analytics, or applied AI may also value its progression from foundational ideas to models, evaluation, and real-world considerations.

For a grounded introduction to the ideas, methods, and responsibilities involved in applying AI to healthcare, this edition brings the essential pieces into one carefully organized discussion.