A practitioner's roadmap for building healthcare AI that earns trust instead of spending it
Artificial intelligence is now woven into the everyday work of medicine - reading scans, flagging risk, predicting deterioration, and personalizing treatment. But every one of those gains is purchased with data: electronic health records, medical images, genomic profiles, and a growing stream from wearable devices. Privacy-Preserving AI in Healthcare argues that this trade-off is not fixed. Drawing on regulatory practice, cryptography, and real deployments, Ajit Sahu and Sanjoy Mukherjee lay out how healthcare organizations can capture AI's clinical upside without exposing the patients behind the data.
The book opens by separating two ideas that are routinely conflated - data security and data privacy - and grounds that distinction in the regulatory landscape healthcare leaders actually operate under, from HIPAA and GDPR to HITECH and CPRA. From there it builds a technical toolkit: anonymization and de-identification, differential privacy, and the cryptographic foundations (encryption, hashing, digital signatures, key management) that make data trustworthy without making it inert. A dedicated risk-assessment framework, including a ready-to-use privacy impact assessment template, ties the technical material back to organizational accountability.
The middle chapters go deep on the two techniques doing the most practical work in the field today. Federated learning is examined end to end - architecture, client–server communication, model aggregation, and security mechanisms - illustrated with case studies including EXAM AI's COVID-19 prediction model, Mount Sinai's federated EHR analysis, NVIDIA FLARE, and the MELLODDY pharmaceutical collaboration. Cryptographic approaches follow, covering homomorphic encryption and secure multi-party computation, with a candid look at the performance and scalability costs these methods impose and the optimization strategies (batching, model compression, hardware acceleration) that make them viable at scale.
The second half moves from technique to practice. A chapter on clinical data and EHR systems addresses interoperability, data-sharing barriers, and the security architecture needed to defend patient records in production. Applications follow across medical imaging, predictive analytics, drug discovery and clinical trials, and remote patient monitoring - each paired with the specific privacy risks that domain introduces. The book closes by looking forward: synthetic data generation, quantum-safe cryptography, and the ethical terrain of bias, fairness, and accountability, ending in a set of policy recommendations built around privacy-by-design.
Written for healthcare professionals, technologists, researchers, and students working at the intersection of AI and patient data, the book pairs each chapter with a summary and review questions, making it as useful as a working reference as it is as a first read. Its central claim is a practical one: privacy-preserving techniques are not a constraint on healthcare AI - they are what makes healthcare AI deployable, defensible, and worthy of patient trust.
