Molecular Pathology, Artificial Intelligence, and New Technologies in Hematologic Diagnostics: Translational...

Area of Science:

• Hematopathology
• Molecular Diagnostics
• Clinical Laboratory Science

Background:

• Hematologic disease diagnostics integrate clinical, morphologic, flow cytometry, and molecular data.
• Genomics is central to current hematologic disease classification systems (WHO HAEM5, ICC).
• Laboratories face increasing complexity and staffing shortages, driving the need for technological solutions.

Purpose of the Study:

• To examine the application of automation, AI, and digital platforms in hematopathology and molecular diagnostics.
• To assess the translational potential of these technologies for improving diagnostic accuracy and patient care.

Main Methods:

• Literature review of peer-reviewed articles and technical reports (through December 2025).
• Focus on digital morphology, AI for blood/marrow interpretation, AI-enabled flow cytometry, laboratory automation/robotics, and AI in molecular hematopathology.

Main Results:

• Digital morphology shows high concordance with manual review and aids AI-assisted analysis.
• AI in flow cytometry matches expert performance for classifying B-cell neoplasms and acute leukemias.
• Automation and robotics improve laboratory throughput and pre-analytic consistency.
• AI models assist in molecular hematopathology variant interpretation and risk stratification.

Conclusions:

• Artificial intelligence is reshaping hematopathology and molecular diagnostics practice.
• Successful translation requires disease-specific validation and multi-modal models aligned with classification frameworks.
• Laboratory governance ensuring expert oversight is crucial for effective implementation.