Bioinformatics and machine learning in acute myeloid leukemia: advancing diagnosis, outcome prediction, and therapeutic strategies – a literature review

Acute Myeloid Leukemia (AML) is a type of hematologic cancer characterized by diverse molecular and clinical features that influence diagnosis, prognosis, and treatment response. Despite advances in molecular classification and therapeutic strategies, challenges remain in achieving accurate risk assessment, treatment optimization, and clinical translation of emerging biomarkers. This literature review provides a comprehensive overview of the applications of bioinformatics and machine learning (ML) approaches in AML, focusing on their contributions to diagnosis, prognostic prediction, and therapeutic decision-making. A narrative literature review was conducted using PubMed, and Google Scholar to identify relevant publications from January 2015 through 2025. Studies investigating bioinformatics and/or ML-based approaches in AML diagnosis, prognosis, biomarker discovery, and treatment-related applications were reviewed and critically evaluated. Current evidence demonstrates that computational approaches, including conventional ML algorithms, deep learning methods, and multi-omics analyses, have facilitated the identification of molecular signatures, improved disease classification, enhanced risk stratification, and supported prediction of treatment-related outcomes. However, despite promising results, the clinical implementation of AML machine-learning models remains limited due to methodological challenges, including retrospective study designs, batch effects, overfitting, insufficient external validation, data leakage, and limited assessment of clinical utility. Overall, bioinformatics and ML represent powerful tools for advancing AML research and precision medicine. Nevertheless, rigorous validation, transparent reporting, and prospective clinical evaluation are required before these approaches can be integrated into routine AML management.