AI In Leukemia Diagnostics: Complementing the Pathologist's Role

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.