Cybersecurity and Privacy Challenges in AI-Driven Healthcare.
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Abstract
The use of Artificial Intelligence (AI) in healthcare has significantly improved medical diagnostics, patient care, and personalized treatment. However, this progress has introduced important challenges, as AI systems rely on large volumes of sensitive patient data, increasing the risk of cybersecurity breaches and privacy violations.
This study adopts a narrative review approach to examine key cybersecurity and data privacy challenges in AI-driven healthcare systems. It explores major threats such as data breaches, adversarial attacks on AI models, and the re-identification of patients from supposedly anonymized data. Ethical concerns related to the collection and use of patient information are also discussed, alongside the strengths and limitations of current mitigation strategies, including federated learning, blockchain technology, differential privacy, and advanced encryption techniques.
Drawing on existing literature, the study identifies critical gaps in current security and privacy frameworks and emphasizes the need for stronger regulatory structures, ethical governance, and practical security measures to support the safe and responsible deployment of AI in healthcare.
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