DATA PRIVACY ISSUES IN HEALTHCARE AI SYSTEMS

Abstract

As we all know that AI has the power to improve various fields in healthcare such as diagnosis, prognosis and workflow. It also brings serious risks to our data privacy in various ways. Since we all are aware of significance of healthcare data, it has become very important to keep our data safe. Data is easily identifiable and often it shared between different organizations. In this article we will focus on the main privacy challenges that AI healthcare system are facing nowadays. It summarizes technical and organizational strategies in order to reduce various risk such as differential privacy, secure computation, federated learning and many more. The article also evaluates the trade-off between usefulness and privacy. In addition, it also provide a research agenda and practical recommendations for using healthcare AI in a way that respects privacy. We reach the conclusion that the protection of privacy requires a multi-faceted strategy that integrates technological measures, adherence to laws, proper management of data and periodic assessments. The rapid adoption of Artificial Intelligence (AI) in healthcare, fueled by the massive volumes of patient data (Big Data), promises revolutionary advancements in diagnostics, personalized medicine, and operational efficiency. However, this dependence on sensitive, personally identifiable information (PII) presents profound and complex data privacy issues. This paper reviews the critical privacy challenges inherent in healthcare AI systems, including the risks associated with data breaches, re-identification of anonymized data, and the limitations of traditional consent models for complex, secondary data use. Furthermore, it addresses the regulatory compliance hurdles imposed by frameworks like HIPAA and GDPR, which are often ill-equipped to govern the decentralized data flows and proprietary algorithms of modern AI platforms. The discussion highlights the urgent need for robust technical solutions—such as Federated Learning and Differential Privacy—combined with enhanced legal and ethical governance to maintain patient trust, ensure compliance, and secure the invaluable data required for beneficial AI development.

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