Privacy-Preserving Deep Learning for Disease Diagnosis in Medical Imaging: A Systematic Review

Medical imaging plays a crucial role in the diagnosis of various diseases, including cancer, cardiovascular conditions, and respiratory disorders such as asthma, pneumonia, and even COVID-19. Nowadays, Deep Learning (DL) has demonstrated remarkable accuracy in disease detection and diagnosis using medical imaging such as X-rays, CT scans, and MRIs. However, DL models mostly require access to sensitive patient data not only to capture the images but also to store them in the data cloud, which raises major privacy concerns. To solve this problem, several techniques such as Differential Privacy (DP) and Federated Learning (FL), have been developed. These methods allow models to learn useful patterns without exposing personal details. Building on these developments, the key gap this review targets is the lack of a recent, side-by-side analysis that connects medical tasks, DL architectures, FL designs, and DP budgets with real-world performance and resilience against privacy attacks. Motivated by this gap, we conducted a systematic review of the literature (SLR) of studies published between 2023 and 2025 that used DL principles with privacy-preserving methods for medical diagnosis. Utilizing the PRISMA framework, we reviewed 23 peer-reviewed articles and grouped them into four categories: included COVID-19, skin lesions, Alzheimer’s disease, and other conditions such as diabetic retinopathy, kidney disease, and heart problems. We sort results by modality, model family, FL topology, DP mechanism, and reported threat model. Furthermore, the review significantly represents that CNN-based models with DP and FL methods are the most common choices, and developed pipelines with these models often achieve accuracy above 90%. At the same time, the review found gaps such as limited real-world testing, a lack of diverse datasets, and underuse of advanced DP approaches. Based on these results, we recommend building larger and more varied datasets, improving privacy-utility trade-offs, and testing models in clinical environments.