Healthcare computerization has generated unprecedented volumes of sensitive information, such as electronic health records (EHRs) and genomics profiles, medical imaging or internet-of-things (IoT)-connected patient monitors. Even though the digital transformation can streamline healthcare systems, and simplify clinical decision-making, it also exposes them to cyberattacks and unauthorized access, as well as data misuse. Healthcare data is highly financial and strategic in value; hence, the need to defend against cybercriminals makes the procedures employed to guarantee its protection critical. In that respect, the deep-learning approach has already become a powerful tool of protecting the healthcare data, detecting anomalies, malicious intrusions, data encryption, and conducting privacy-conscious data analytics. This paper will provide a survey of how deep learning is being used to enhance the safety of healthcare data. It talks about convolutional neural networks (CNNs), medical image watermarking, recurrent neural networks (RNNs), anomaly detection in health IoT networks, and autoencoders, intrusion detection. The paper also cites the advantages of deep learning in adapting dynamically changing cyberthreats, learning complex attack signatures, and working on large heterogeneous data sets. Meanwhile, it also admits the shortcomings of model interpretability, computational complexity, and vulnerability to adversarial attacks, including. These outputs serve as a confirmation that any significant advancement in data protection will occur via deep learning, yet their application alongside explainable AI, blockchain, and federated learning will be a decisive factor in establishing trust, transparency and resilience. The author of this paper tries to justify that by applying deep learning to the problem of healthcare data security, sensitive information can be secured, and patients will not lose their trust, and the opportunity to adhere to the rules of data protection in the digital era will be obtained.
Sidhu Sidhu (Thu,) studied this question.