Building and evaluating an artificial intelligence algorithm:A practical guide for practicing oncologists

摘要

The use of machine learning and deep learning has enabled many applications,previously thought of as being impossible.Among all medical fields,cancer care is arguably the most significantly impacted,with precision medicine now truly being a possibility.The effect of these technologies,loosely known as artificial intelligence,is particularly striking in fields involving images(such as radiology and pathology)and fields involving large amounts of data(such as genomics).Practicing oncologists are often confronted with new technologies claiming to predict response to therapy or predict the genomic make-up of patients.Understanding these new claims and technologies requires a deep understanding of the field.In this review,we provide an overview of the basis of deep learning.We describe various common tasks and their data requirements so that oncologists could be equipped to start such projects,as well as evaluate algorithms presented to them.