Laboratory-first detection of myeloid neoplasms: growing evidence from automated hematology, artificial intelligence, and flow cytometry

Early diagnosis of myeloid neoplasms is challenging because initial clinical manifestations are often subtle or nonspecific. Accumulating evidence indicates that abnormalities detected by routine clinical laboratory testing frequently precede overt morphological or clinical progression. Advances in automated hematology analyzers, together with cytogenetic, molecular, and flow cytometric techniques, now enable laboratories to detect early disease signatures with increasing precision. This mini-review summarizes evidence supporting a laboratory-first approach to myeloid neoplasm detection and highlights how early laboratory-derived signals can shorten diagnostic latency and support clinical decision-making. Relevant studies were identified through a narrative review of the literature, with emphasis on routine hematologic findings associated with early diagnosis. Automated hematology analyzers provide initial alerts through abnormal cell population patterns, histogram changes, and analyzer flags, often before circulating blasts or overt dysplasia become evident. Artificial intelligence (AI)–based models further enhance interpretation of routine laboratory data, enabling rapid triage and risk stratification. Cytogenetic abnormalities establish clonality in morphologically equivocal cases, while flow cytometry extends the laboratory-first paradigm by enabling detection of subclinical disease and measurable residual disease. Together, these laboratory-driven strategies form an integrated framework that promotes earlier intervention, risk-adapted management, and improved outcomes in patients with myeloid neoplasms.