Physicians and medical experts are starting to incorporate algorithms and machine learning in many parts of the health care system, including experimental models to analyze images from X-rays and brain scans. The goal is to use computers to improve detection and diagnosis of patients’ ailments. Such models are trained to identify tumors, skin lesions and more, using databases full of reference scans or images. But there are also potential biases within the data that could result in skewed diagnoses from these machine learning models. Marketplace’s Kimberly Adams spoke to María Agustina Ricci, a biomedical engineer who is pursuing a Ph.D. at the Hospital Italiano de Buenos Aires in Argentina. She has studied how the disparities between low-income and developed countries could worsen, or create, these biases.