aceRobustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications
The current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs. We examine factors that may influence model reliability, including model complexity, training data quality, and hyperparameters; we also examine security concerns related to adversarial attacks that aim to deceive models along with privacy attacks that seek to extract sensitive information. Resea…
Adversarial training, input preprocessing, data augmentation and uncertainty estimation are being explored to enhance robustness and reliability features of deep learning medical diagnosis systems built on TensorFlow and PyTorch.
Deep learning models for medical diagnosis struggle to maintain performance when faced with adversarial or noisy inputs, and are vulnerable to adversarial attacks that deceive models and privacy attacks that extract sensitive patient information.
Evidence
- Peer-reviewedArtificial Intelligence Review2024-11-08
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Truvace Impact Record TRV-2026-0276, v1: “Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications.” Truvace, 2026-07-19. /record/TRV-2026-0276 (accessed at citation time). sha256 fc77c2893a67b0cc…
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