  The increasing complexity of Artificial Intelligence models poses challenges
to interpretability, particularly in the healthcare sector. This study
investigates the impact of deep learning model complexity and Explainable AI
(XAI) efficacy, utilizing four ResNet architectures (ResNet-18, 34, 50, 101).
Through methodical experimentation on 4,369 lung X-ray images of
COVID-19-infected and healthy patients, the research evaluates models'
classification performance and the relevance of corresponding XAI explanations
with respect to the ground-truth disease masks. Results indicate that the
increase in model complexity is associated with a decrease in classification
accuracy and AUC-ROC scores (ResNet-18: 98.4%, 0.997; ResNet-101: 95.9%,
0.988). Notably, in eleven out of twelve statistical tests performed, no
statistically significant differences occurred between XAI quantitative metrics
- Relevance Rank Accuracy and the proposed Positive Attribution Ratio - across
trained models. These results suggest that increased model complexity does not
consistently lead to higher performance or relevance of explanations for
models' decision-making processes.
