<ul><li>Differential privacy (DP) is crucial for safeguarding sensitive patient data in medical deep learning.</li><li>Balancing privacy, utility, and fairness has become a significant challenge as clinical models become more data-dependent.</li><li>A scoping review synthesized recent developments in using DP for medical deep learning, focusing on DP-SGD and alternative mechanisms in centralized and federated settings.</li><li>While DP can maintain performance in structured imaging tasks, strict privacy measures often lead to significant performance degradation, especially in underrepresented or complex data modalities.</li></ul>

Differential Privacy for Deep Learning in Medicine

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