Acute Myeloid Leukemia is an rapidly progressing blood cancer with diverse biological behavior characterised by marked biological heterogeneity, rapid clinical progression, and a high tendency for relapse. Although advances in molecular diagnostics and risk-adapted treatment strategies have improved disease characterization, doctors still face major challenges in achieving accurate prognosis, early detection of relapse, and truly individualized treatment planning. artificial intelligence (AI), particularly machine learning and deep learning approaches, has begun to influence oncology practice. These tools are capable of integrating large and complex datasets, including clinical findings, genomic profiles, and laboratory parameters, to support more refined disease classification, improved risk assessment, and better informed treatment decisions. AI based systems also aid in recognizing clinically significant genetic alterations such as FLT3, NPM1, and TP53, thereby contributing to the development of precision medicine approaches. Beyond diagnosis and risk stratification, AI has shown potential in predicting disease relapse, tracking minimal residual disease, and optimizing outcomes following hematopoietic stem cell transplantation. Despite these promising applications, its integration into routine clinical practice remains limited due to issues such as inconsistent data, insufficient transparency in model interpretation, and the need for stronger external validation.AI models can identify mutations such as FLT3 or NPM1 and link them to treatment response patterns.

Acute Myeloid Leukemia, Artificial Intelligence, Machine Learning, Deep Learning, Precision Medicine, Risk Stratification, Genomic Profiling, Targeted Therapy, Minimal Residual Disease, Clinical Decision Support Systems

Estey E, Döhner H. Acute myeloid leukemia. Lancet. 2021

Short NJ, Rytting ME, Cortes JE. Acute myeloid leukemia. J Clin Oncol. 2020.

Döhner H, et al. Diagnosis and management of AML in adults: 2022 ELN recommendations. Blood. 2022.

Radakovich N, et al. AI and machine learning in AML risk prediction. Best Pract Res Clin Haematol. 2020.

Gillies RJ, et al. Radiomics and AI in oncology. Nat Rev Cancer. 2021.

Papaemmanuil E, et al. Genomic classification and prognosis in AML. N Engl J Med. 2016.

Arber DA, et al. WHO classification of myeloid neoplasms. Blood. 2022.

Tallman MS, et al. Acute myeloid leukemia therapy. N Engl J Med. 2019.

Short NJ, et al. Acute myeloid leukemia: current treatment approaches. J Clin Oncol. 2020.

He J, et al. Artificial intelligence in hematopathology and leukemia diagnosis. Leukemia. 2021.

Gillies RJ, et al. Artificial intelligence in oncology decision-making. Nat Rev Cancer. 2021.

Gerstung M, et al. Integrative data analysis for cancer outcome prediction. Nat Med. 2019–2021 updates.

Topol EJ. High-performance medicine and AI integration. Nat Med. 2019–2020 updates.

Gibson CJ, et al. Predictive modelling in stem cell transplantation outcomes. J Clin Oncol. 2019–2021 updates.