DefinePK

Abstract

The convergence of Artificial Intelligence (AI) and healthcare has ushered in a transformative era in medical diagnostics, offering unprecedented precision, speed, and efficiency. From identifying early-stage cancers through radiological imaging to predicting genetic disorders and personalizing treatment plans, AI is fundamentally reshaping modern medicine. However, this technological leap comes with a parallel rise in legal and ethical complexities, particularly concerning patient data privacy, algorithmic transparency, and informed consent.This paper undertakes a multidimensional legal and ethical analysis of AI-driven medical diagnostics, with a special focus on data governance under the European Union’s General Data Protection Regulation (GDPR) and the enduring principles of medical ethics. It explores how core GDPR mandates including data minimization, purpose limitation, the right to explanation, and the right to erasure interact, and often conflict, with the operational realities of AI systems that function as data-intensive, opaque “black boxes.” The paper further investigates Article 22 of the GDPR, which limits fully automated decision-making, and examines the growing tension between legal mandates and algorithmic logic.Beyond regulatory scrutiny, the paper delves into ethical concerns such as loss of patient autonomy, the weakening of meaningful informed consent, and the risk of algorithmic bias leading to systemic discrimination particularly for underrepresented or vulnerable populations. Through case studies from jurisdictions like the UK (e.g., DeepMind-NHS controversy), the US (racial bias in AI triage systems), and India (challenges under the Digital Personal Data Protection Act, 2023), the study provides a comparative analysis of how various health systems are grappling with these issues.In bridging the legal and ethical dimensions, this research contributes original policy insights and practical recommendations aimed at strengthening accountability, ensuring fairness, and promoting transparency in AI-enabled healthcare. It advocates for regulatory modernization, mandatory algorithmic audits, explainable AI protocols, and patient-centric system design. The paper ultimately argues that while AI holds revolutionary promise in diagnostics, its deployment must be tempered by strong legal safeguards and ethical foresight to ensure that technological advancement does not come at the cost of patient rights, equity, or dignity