Figure 2:

ML, step by step: ML is a type of AI that enables computer systems to learn from training data without explicit programming. DL is a more specific subset of ML that uses algorithms with multiple layers, simulating the complexity of the human brain. In healthcare, popular DL algorithms include artificial neural networks (ANNs), convolutional neural networks (CNNs) and recurrent neural networks (RNNs). There are three main types of ML algorithms: supervised learning, unsupervised learning and reinforcement learning. Supervised learning uses labeled data to train classification models, while unsupervised learning identifies clusters in unstructured data. Reinforcement learning relies on trial and error to learn from feedback. Once the model has been selected and data has been inputted, it must be trained with the data to produce accurate results. The model should then be validated to ensure it can generalize to new data and be reproducible for reliable clinical use.