How can we … address dataset scarcity challenges and mitigate algorithmic bias to build better AI healthcare tools?
31 March 2025
Artificial Intelligence models are only as fair as the data on which they are trained. They are vulnerable to biases in training data, often reflecting deep-rooted systemic inequalities, human prejudices, and socio-economic disparities. This is a serious concern in healthcare, where algorithmic decisions can directly affect patient outcomes and can lead to severe, even catastrophic, consequences to human health. One challenge is ensuring that models that will be used in a healthcare setting are trained using data that represents patients. An important first step in addressing bias from underrepresentation in datasets is to understand why it can be difficult to acquire large and diverse datasets from which to develop AI algorithms.
