## 5 Results and Discussion
### Contextualizing Vulnerability and Guiding Actionable Insights  
Our findings highlighted the varied impact of different attack types on model performance. The adversarial FGSM and evasion attacks resulted in high false positive rates, as shown in Figure 5. In real-world scenarios, such incidents can lead to unnecessary biopsies, wasting resources, and causing patient distress. Conversely, data poisoning gradually erodes diagnostic reliability without inducing drastic performance drops. This type of impact can undermine trust over time. The PAVI contextualizes these vulnerabilities by translating them into actionable insights. For instance, high PAVI values associated with adversarial attacks underscore the urgency of integrating adversarially robust training. In contrast, lower PAVI values for data poisoning suggest a focus on proactive data integrity measures. By quantifying the relative severity of performance degradation, the HAVA guides the development of tailored defenses that address specific vulnerabilities. The algorithm's holistic evaluation of attack impacts and defense effectiveness ensures that AI systems in healthcare can be continuously improved rather than discarded after compromise. The utility of the PAVI in healthcare can be exemplified through a breast cancer diagnostic system. Suppose adversarial attacks cause a PAVI of 38.60%, resulting in a significant misclassification of malignant cells as benign. This outcome directly jeopardizes patient outcomes by delaying life-saving treatments. By identifying adversarial attacks as the primary vulnerability, the PAVI enables the prioritization of adversarial training during model re-training. Subsequently, testing and validation confirm whether the mitigation strategy reduces the PAVI to acceptable levels, such as below 10%. This iterative refinementensures the AI model's safety and reliability in real-world applications to safeguard patients' health and maintain trust in AI-driven diagnostics. The PAVI's importance is particularly pronounced in healthcare, where diagnostic errors have life-altering consequences. For example, high PAVI values under adversarial attacks could prompt the implementation of adversarial training to mitigate the misclassification of malignant cells as benign to prevent delayed treatments. Similarly, PAVI's insights into adversarial evasion attack vulnerabilities could inspire the development of noise-resistant input preprocessing techniques to reduce false positives that lead to unnecessary medical interventions. By integrating the PAVI into mitigation workflows, the HAVA empowers researchers and practitioners to create resilient AI systems capable of navigating real-world deployment challenges. We assume that the HAVA and the PAVI can be customized and applied by future research works according to their requirements. For example, a model's PAVI can be categorized into low, medium and high by setting a range of values considering its criticality. The future work in this domain faces challenges and opportunities. Future research could expand on this work by developing and integrating advanced defense mechanisms, such as adversarial training, federated learning, and privacy-preserving techniques by considering the HAVA framework. Testing the HAVA across diverse medical imaging datasets and conditions will also ensure its scalability and applicability in real-world scenarios. While this study focused on the PAVI as a holistic measure of model robustness, future work could incorporate additional performance metrics, such as precision, recall, and F1 score, to comprehensively evaluate model performance under different attack scenarios. Future efforts could also explore defense strategies to mitigate the vulnerabilities identified in this study. For instance, adversarial defenses, such as input preprocessing, defensive distillation, and ensemble modeling, could be integrated with the HAVA to strengthen the resilience of DL models in healthcare applications.