Computer Vision News Computer Vision News 40 AIMMES 2024 Workshop on AI bias Mariachiara Di Cosmo is a PhD candidate at Università Politecnica delle Marche, focusing on the application of deep learning techniques in medical image analysis. She accepted to sum up the key insights from her contribution at the AI Fairness Cluster Inaugural Conference, presented within the workshop: AI Bias - Measurements, Mitigation, Explanation Strategies. by Mariachiara Di Cosmo In the dynamic field of medical imaging, AI is gaining prominence in prenatal diagnostics. Fetal ultrasound (US) is crucial in prenatal care, providing visualization and monitoring of fetal development within the womb. AI holds promise in expanding US diagnostic capabilities, increasing accuracy and efficiency, while addressing challenges associated with this complex operator-dependent technique. As evidence of this, a burgeoning body of literature emerged recently also thanks to the availability of benchmark international datasets open to research community. However, for a researcher in medical imaging analysis and deep learning developer, critical questions arise: ➢ Will prenatal diagnostics truly take advantage from AI tools? ➢ Can we ensure AI models benefit all populations and prenatal contexts equally? ➢ Are we designing “fair” diagnostic support systems, considering minorities, unusual fetal anatomies and real clinical US setup? Looking for answers and guidelines, we examine public fetal US datasets, used for algorithm development. The integrity of AI models outcomes heavily depends on training datasets. Common biases include a lack of demographic representativeness, leading to models performing inequitably across different populations or hospitals. Are We Building Biases in AI-Driven Fetal Diagnostics? Uncovering Ethical Issues in Public Ultrasound Datasets
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