Pasquale Niscola, Valentina Gianfelici, Roberta Laureana, Marco Giovannini, Carla Mazzone, Fabio Efficace, Maria Ilaria Del Principe
Acute Myeloid Leukemia (AML) is a heterogeneous group of aggressive blood-related cancers that arise from the hematopoietic system, requiring specific treatments due to their genetic diversity and complexity. Artificial intelligence (AI) has emerged as a transformative technology in healthcare, with considerable potential to help manage AML. The application of AI approaches, such as Machine Learning (ML) models and Deep Learning (DL) algorithms, has been shown to aid risk stratification, diagnosis, treatment planning, and surveillance. This review highlights recent developments in AI applications for the personalized management of AML. We focus specifically on three major axes of personalization in AML: (1) the use of predictive models combining different data sources to improve prognostic assessment and guide risk-adaptive treatments; (2) prediction of treatment responses to various therapies based on data analysis; and (3) the use of AI for monitoring and adaptive trials in AML patients.