Charbel Mourad

Elodie Germani, Ilayda Selin Türk, Fatima Zeineddine et al.

Over the past decades, computer-aided diagnosis tools for breast cancer have been developed to enhance screening procedures, yet their clinical adoption remains challenged by data variability and inherent biases. Although foundation models (FMs) have recently demonstrated impressive generalizability and transfer learning capabilities by leveraging vast and diverse datasets, their performance can be undermined by spurious correlations that arise from variations in image quality, labeling uncertainty, and sensitive patient attributes. In this work, we explore the fairness and bias of FMs for breast mammography classification by leveraging a large pool of datasets from diverse sources-including data from underrepresented regions and an in-house dataset. Our extensive experiments show that while modality-specific pre-training of FMs enhances performance, classifiers trained on features from individual datasets fail to generalize across domains. Aggregating datasets improves overall performance, yet does not fully mitigate biases, leading to significant disparities across under-represented subgroups such as extreme breast densities and age groups. Furthermore, while domain-adaptation strategies can reduce these disparities, they often incur a performance trade-off. In contrast, fairness-aware techniques yield more stable and equitable performance across subgroups. These findings underscore the necessity of incorporating rigorous fairness evaluations and mitigation strategies into FM-based models to foster inclusive and generalizable AI.

Mouin Jammal, Antoine Saab, Cynthia Abi Khalil et al.

Background: The adherence of clinicians to clinical practice guidelines is known to be low, including for the management of COVID-19, due to their difficult use at the point of care and their complexity. Clinical decision support systems have been proposed to implement guidelines and improve adherence. One approach is to permit the navigation inside the recommendations, presented as a decision tree, but the size of the tree often limits this approach and may cause erroneous navigation, especially when it does not fit in a single screen. Methods: We proposed an innovative visual interface to allow clinicians easily navigating inside decision trees for the management of COVID-19 patients. It associates a multi-path tree model with the use of the fisheye visual technique, allowing the visualization of large decision trees in a single screen. To evaluate the impact of this tool on guideline adherence, we conducted a randomized controlled trial in a near-real simulation setting, comparing the decisions taken by medical students using Orient-COVID with those taken with paper guidelines or without guidance, when performing on six realistic clinical cases. Results: The results show that paper guidelines had no impact (p=0.97), while Orient-COVID significantly improved the guideline adherence compared to both other groups (p<0.0003). A significant impact of Orient-COVID was identified on several key points during the management of COVID-19: ordering troponin lab tests, prescribing anticoagulant and oxygen therapy. A multifactor analysis showed no difference between male and female participants. Conclusions: The use of an interactive decision tree for the management of COVID-19 significantly improved the clinician adherence to guidelines. Future works will focus on the integration of the system to electronic health records and on the adaptation of the system to other clinical conditions.