The diagnosis of hematological disorders is currently established from the combined results of different tests, including those assessing morphology (M), immunophenotype (I), cytogenetics (C), and molecular biology (M) (collectively known as the MICM classification). In this workflow, most of the results are interpreted manually (i.e., by a human, without automation), which is expertise-dependent, labor-intensive, time-consuming, and with inherent interobserver variability. Also, with advances in instruments and technologies, the data is gaining higher dimensionality and throughput, making additional challenges for manual analysis. Recently, artificial intelligence (AI) has emerged as a promising tool in clinical hematology to ensure timely diagnosis, precise risk stratification, and treatment success. In this review, we summarize the current advances, limitations, and challenges of AI models and raise potential strategies for improving their performance in each sector of the MICM pipeline. Finally, we share perspectives, highlight future directions, and call for extensive interdisciplinary cooperation to perfect AI with wise human-level strategies and promote its integration into the clinical workflow.

Artificial intelligence  /  Machine learning  /  Deep learning  /  Laboratory hematology  /  Diagnosis  /  Prognosis  /  Clinical workflow  /  MICM classification