Title:AI-Augmented Cytogenetics in Hematologic Malignancies: A Diagnostic Paradigm Shift
Volume: 23
Author(s): Jiun Kang*
Affiliation:
- Department of BioMedical Laboratory Science, Korea Nazarene University, Cheonan-city, Chungnam, 330-718, South Korea
Keywords:
Artificial intelligence, cytogenetics, hematologic malignancies, karyotyping, fluorescence in situ hybridization, chromosomal microarray analysis, optical genome mapping.
Abstract:
Cytogenetic testing plays a critical role in the diagnosis and risk stratification
of hematologic malignancies. However, conventional techniques are inherently constrained
by technical limitations, including low resolution, labor-intensive workflows, and
inter-observer variability. Recent advances in artificial intelligence, particularly deep
learning-based approaches, have shown promise in addressing these limitations by enhancing
image analysis, automating interpretation, and standardizing complex workflows.
Many studies have demonstrated that AI-integrated platforms significantly reduce diagnostic
turnaround time, detect cryptic or subclonal chromosomal aberrations, and improve
interpretive concordance across laboratories. Despite these advantages, barriers, such as
limited model interpretability, data heterogeneity, and regulatory challenges, remain. Rather
than replacing human expertise, AI is emerging as a powerful adjunct that strengthens
the accuracy and reproducibility of genomic assessments and promotes timely, individualized
therapeutic decision-making. As the technology matures, AI is expected to become
an integral component of cytogenetic diagnostics, driving a shift toward more efficient,
scalable, and precision-guided clinical workflows in hematologic oncology.
