Machine Learning is a type of Artificial Intelligence. Machine Learning involves sophisticated algorithms which can be trained to sort information, identify patterns, and make predictions within large sets of data. Machine learning algorithms are used by researchers to build models based on sample or training data in order to make predictions or decisions, without being explicitly programmed to do so. This approach can be “supervised” or “unsupervised” learning, which refers to the labeling or not labeling input data. Supervised machine learning including a level of human intervention and adjustment to the algorithm.
One example of machine learning Is an algorithm model that is trained to identify cancer risk by being given a set of medical images that are correctly labeled as “cancerous” or “noncancerous”. The program “learns” from these images and can then be given larger data sets to sort through and find patterns that indicate a higher risk of cancer.
Machine learning models can be developed using a variety of open source and proprietary software. Python, C++, and Java are commonly used languages for machine learning software.
This website provides a brief introduction to machine learning, including a bit of history and explanation of different types: Pant, A. (2019, January 22). Introduction to machine learning for beginners. Medium. Retrieved May 1, 2022, from https://towardsdatascience.com/introduction-to-machine-learning-for-beginners-eed6024fdb08
This article provides a basic explanation of machine learning and its use in medicine: Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/nejmra1814259