As artificial intelligence transforms the way medical diagnoses are made, the imaging data at the heart of this transformation raises a question that healthcare systems across the world have not yet fully answered: who protects the patient once the image has been taken?
Dr M V N Nagalakshmi, Assistant Professor, Department of Management at the Paari School of Business, examines this urgent and underexplored question in her book chapter titled Healthcare Services Utilizing State-of-the-Art Image Analysis: A Dilemma Regarding the Right to Privacy. The chapter appears in Privacy-Preserving AI in Healthcare, published by IGI Global Scientific Publishing in 2027.
The chapter investigates how AI-driven medical image analysis, spanning X-ray, CT, PET, MRI, and ultrasound technologies, is fundamentally reshaping diagnostics in healthcare, while simultaneously exposing critical gaps in privacy protection once patient imaging data is collected, stored, or transferred. Drawing on a review of prior literature on deep learning in medical imaging, the chapter surveys India’s domestic privacy legal framework, grounded in part in the A.P. Shah Committee’s privacy principles and Article 21 of the Indian Constitution.
To understand how well real-world practice aligns with stated privacy principles, the chapter also reports on a qualitative field study, an anonymous survey conducted with record-keepers at a diagnostic centre, three hospitals, and one large public hospital. The findings reveal a significant and concerning gap between written policy and actual institutional practice. In response, the chapter proposes a visual-encryption workflow for securing healthcare images, structured as a clear pipeline: preprocess, compress, encrypt, transfer, decrypt, decompress, and evaluate accuracy.
Why This Chapter Matters
This chapter is significant because it captures one of the defining tensions in contemporary healthcare: the accelerating adoption of AI and imaging technologies against a governance landscape that has not kept pace. It frames data privacy not merely as a compliance obligation, but as both a trust-based competitive asset and a strategic liability risk for healthcare institutions. Its call for state-coordinated public-private collaboration illustrates stakeholder governance in action, and its documentation of the gap between written policy and hospital practice offers a valuable strategy-as-practice case study. The India-based setting adds important emerging-market context to comparative regulatory and technology-adoption research.
For researchers in strategy, institutional theory, and dynamic capabilities, the chapter offers a live case study of the innovation-regulation gap at work in one of the world’s most sensitive data environments.
Who Will Find This Chapter Valuable
This chapter will be a useful and timely resource for healthcare IT and informatics professionals implementing image storage and transfer systems, hospital administrators and medical record-keepers responsible for compliance, health law and policy researchers and students with a focus on India’s privacy regulatory landscape, AI and machine learning researchers in medical imaging who need privacy-by-design context, regulators and policymakers working on healthcare data protection frameworks, and graduate students in health informatics, law, or biomedical engineering programmes.
Read the chapter here.