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Updated: Jul 3, 2026

Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia
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AI In Leukemia Diagnostics: Complementing the Pathologist's Role.

Sayandeep K Das1, Kusal K Das2

  • 1Department of Pathology, BLDE (Deemed To Be University), Shri B. M. Patil Medical College Hospital and Research Centre, Vijayapura, Karnataka, India.

International Journal of Laboratory Hematology
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances leukemia diagnostics by automating tasks and standardizing interpretation. A human-in-the-loop approach ensures expert oversight, leading to more accurate and personalized patient care.

Keywords:
artificial intelligencedigital pathologyflow cytometryhematopathologyhuman–AI collaborationleukemiamedical education

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Area of Science:

  • Hematopathology
  • Medical Informatics
  • Computational Biology

Background:

  • Artificial intelligence (AI) is transforming leukemia diagnostics across various techniques.
  • Current diagnostic workflows require significant expert time for quantitation and interpretation.

Purpose of the Study:

  • To review the integration of AI tools in leukemia diagnostics.
  • To outline a "human-in-the-loop" workflow for AI-assisted hematopathology.
  • To address educational needs for sustainable AI adoption.

Main Methods:

  • Review of contemporary AI applications in digital morphology, flow cytometry, and multi-omics analysis.
  • Proposal of a "human-in-the-loop" workflow integrating AI into laboratory information systems.
  • Mapping of validator-integrator roles and training competencies for hematopathologists.

Main Results:

  • AI tools can automate quantitation, identify patterns, and standardize interpretation in leukemia diagnostics.
  • A human-in-the-loop model ensures expert validation, mitigating bias and resolving discordant findings.
  • Essential skills for future hematopathologists include data-science literacy, AI output appraisal, and ethical governance.

Conclusions:

  • AI can augment, not replace, the diagnostic role of leukemia specialists.
  • Targeted education and rigorous validation are crucial for successful AI implementation.
  • AI-assisted hematopathology promises more timely, reproducible, and personalized patient care.