Representation in breast cancer clinical trials
OA Version
Citation
Abstract
Artificial Intelligence (AI) has transformed health care with improved predictions, diagnostics, and treatments by using advanced analytics and models on data. In cancer, especially breast cancer treatment, clinical trials are an essential resource. Yet, very few people from diverse backgrounds have been included in these studies, which can lead to biases against underrepresented groups in health innovations. This thesis explores demo- graphic disparities in breast cancer clinical trials across the United States by analyzing representation patterns and what that means for equity in clinical research. Findings emphasize the need for inclusive trial designs and robust data collection with the aim of guiding equitable and e↵ective treatment development. I also point out that, while achieving equitable representation in clinical trials would be a great improvement, it is still not enough to support machine-learning classifiers used in diagnosis and genetic analysis, which generally require equal numbers of cases in training data for each class.
Description
2025
License
Attribution-NonCommercial-ShareAlike 4.0 International