From a Static Regulation Model Towards Regulation as Iterative Checkpoints
Artificial Intelligence (AI) has the power to improve health outcomes for patients because AI can distinguish patterns in data that are not discernible to the clinician. These patterns rely on a supply of health data to train machines that learn responses to diagnose, predict, or perform more complex medical tasks.
Traditional, or non-adaptive, Machine Learning (ML) utilizes two separate paths--training and prediction, whereas adaptive AI uses a single path process that monitors and learns the new changes made to the input and output values and their associated characteristics.
