Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives

Abstract

SummaryMachine learning (ML) approaches have been applied in the diagnosis and prediction of haematological malignancies. The consideration of ML algorithms to complement or replace current standard of care approaches requires investigation into the methods used to develop relevant algorithms and understanding the accuracy, sensitivity and specificity of such algorithms in the diagnosis and prognosis of malignancies. Here we discuss methods used to develop ML algorithms and review original research studies for assessing the use of ML algorithms in the diagnosis and prognosis of lymphoma.