Adversarial Machine Learning in Healthcare: Risks to AI-Driven Diagnostics and Treatment Plans
Bibliographic record
Abstract
The rapid integration of artificial intelligence (AI) in healthcare has enhanced diagnostics, predictive analytics, and clinical decision-making. However, AI-driven models, particularly deep learning architectures, remain highly vulnerable to adversarial machine learning (AML) attacks, which can result in misdiagnoses, unsafe treatment recommendations, and compromised patient safety. This study systematically evaluates adversarial risks in medical AI, quantifies their impact on model performance, and assesses the efficacy of defense mechanisms. We analyzed CNNs (medical imaging), RNNs (ECG analysis), and Transformer models (clinical NLP) under FGSM, PGD, and JSMA attacks. Results show that the CNN accuracy of 92% was reduced to 40% under JSMA, ECG-based AI performance dropped by 42% under PGD, and Transformer-based NLP models experienced a 30% decline under FGSM. Defense mechanisms such as randomized smoothing and adversarial training improved accuracy by 15% and 14%, respectively, though at high computational costs (1.8× and 1.5× training overhead). Across five independent trials, all degradations were statistically significant (p< 0.01), and ANOVA with Tukey’s HSD confirmed that randomized smoothing and adversarial training significantly outperformed gradient masking (p< 0.01). These findings demonstrate that medical AI systems are highly susceptible to adversarial manipulation and underscore the necessity of robust, efficient, and regulatory-compliant defenses. Strengthening adversarial resilience is critical to ensuring safe, reliable, and ethically responsible deployment of AI in healthcare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".