Background: Artificial intelligence (AI) has emerged as a promising adjunct in diagnostic pathology, particularly in histopathology, hematology, and cytology. However, its real-world performance compared with traditional pathologist-based reporting remains under-explored. Objective: To assess the diagnostic accuracy, efficiency, and concordance between AI-driven image analysis systems and manual reporting by experienced pathologists across three major domains of laboratory medicine. Methods: Clinical cases were prospectively analyzed using both AI image analysis software and conventional pathologist review. Metrics including sensitivity, specificity, turnaround time (TAT), inter-observer variability, and concordance rate were compared. Results: AI demonstrated high concordance with manual reporting in hematology (93%) and cytology (89%), with slightly lower concordance in histopathology (85%). AI significantly reduced TAT (average reduction of 35–50%). However, manual reporting remained superior in recognizing rare variants, artifacts, and complex morphologies. Conclusion: AI-driven image analysis shows promise as a supportive tool for routine diagnostic work, enhancing speed and consistency. Nevertheless, expert pathologist oversight remains essential, especially in complex or borderline cases.

Artificial intelligence (AI), Turnaround time (TAT), Inter-observer variability, Quantitative histomorphometry (QH). Manual, Histomorphometry.