SAFEGUARDING PATIENT PRIVACY IN RADIOLOGY THROUGH EFFECTIVE DATA ANONYMIZATION.

Abstract

The significance of radiology, a science that depends significantly on enormous volumes of sensitive patient data, has increased due to the exponential expansion of AI and big data in healthcare. To guard against identity theft and data misuse, the data from X-rays, MRIs, CT scans, etc. needs strict privacy safeguards. Protecting patient identities and facilitating the safe, moral use of medical data for research, teaching, and AI training depend heavily on data anonymization. Efficient anonymization procedures are supported by advanced capabilities in Enterprise Imaging PACS and DICOM publishing systems, which prefer to keep patient information in DICOM headers and picture overlays rather than embedding it in the images. This division maintains data quality while making de-identification easier.To protect patient trust, adherence to laws like HIPAA and GDPR is both morally and legally required. However, there are difficulties in anonymizing radiological data, such as preserving data utility and handling embedded identifiers. Solutions include techniques like federated learning, pseudonymization, and DICOM header de-identification. It's critical to strike a balance between privacy and innovation so that data may be used to progress AI without compromising individual rights. The future of data security in radiography is being shaped by emerging technologies and partnerships between regulators and healthcare organizations