Building Robust Medical Algorithms

The best datasets have the perfect combination of quantity and quality, as well as enough diversity to be fully representative of the different types of patients that the algorithm will be used for. Obtaining high‐quality data for the training and validation of Artificial intelligence (AI) models is challenging. If the people want to aggregate data from different sources so that they can train and use AI algorithms in healthcare, data standardization will be critical. Federated learning is an up‐and‐coming approach to AI training that aims to protect the privacy of sensitive user data by ensuring that it never leaves their device. Art generated by generative AI can be impossible to distinguish from a painting by humans. Synthetic data is one way of dealing with the issue of creating datasets for algorithm training.