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robustness and security of deep learning models for medical diagnosis against adversarial and noisy inputs

Source article: Robustness 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…

TRV-2026-0276Peer-reviewedPermanent record — cite & verify
Trace impact reading

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P 74The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 72The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications

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The quick read

Published November 8, 2024, this review examines whether deep learning models for medical diagnosis can maintain performance when exposed to adversarial or noisy inputs, analyzing influences such as model complexity, training data quality, and hyperparameters.

It matters because unreliable diagnostic AI could mislead clinical decisions and expose sensitive health data, and while defenses like adversarial training and preprocessing are being explored, the paper notes ongoing limitations in the literature and the need for further work to make medical AI trustworthy, reliable, and stable.

Main points
  • Study focuses on deep learning models for accurate medical diagnosis systems and their ability to maintain performance with adversarial or noisy inputs.
  • Examines factors influencing reliability including model complexity, training data quality, and hyperparameters.
  • Discusses security concerns from adversarial attacks that deceive models and privacy attacks that extract sensitive information.
  • Evaluates defenses such as adversarial training, input preprocessing, data augmentation, uncertainty estimation, and reliability tools for TensorFlow and PyTorch.
Gain

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.

Problem

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.

The rundown

The paper investigates robustness of deep learning models used for medical diagnostics, specifically how model complexity, training data quality, and hyperparameters affect reliability when inputs are adversarial or noisy.

It surveys security threats including deception-focused adversarial attacks and sensitive-information extraction via privacy attacks, and reviews countermeasures like adversarial training, input preprocessing, data augmentation, uncertainty estimation, and framework extensions for TensorFlow and PyTorch, along with existing robustness evaluation metrics.

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