Introduction/Background: Artificial intelligence (AI) and digital image analysis (IA) are increasingly used in pathology to quantify biomarkers such as tumor-infiltrating lymphocytes (TIL), Ki-67, and PD-L1. However, the path from algorithm development to clinical deployment requires careful planning, rigorous validation, and sustained quality control (QC). Practical guidance on bridging this gap safely and reproducibly remains limited.

Methods/Design: Drawing on experience at a dedicated Pathology Image Analysis Laboratory at an academic medical center, this talk presents a practical, stepwise roadmap for developing and deploying IA assays in clinical care. The framework encompasses: (1) assay definition and clinical intent; (2) workflow planning including scanner selection, region of interest, and cutoff determination; (3) algorithm development and parameter optimization; (4) analytical and clinical validation aligned with College of American Pathologists (CAP) Digital Imaging and Artificial Intelligence (DIA) checklist and Clinical Laboratory Improvement Amendments (CLIA) requirements; and (5) ongoing performance monitoring through longitudinal QC.

Results: Analytical validation parameters addressed include accuracy, precision, reproducibility, linearity, analytical sensitivity, and analytical specificity. Real-world case studies illustrate how scanner color calibration drift and customizable analysis parameter variability can alter results in ways that mimic algorithm failure, underscoring the necessity of structured QC frameworks, audit trails, and pathologist oversight.

Conclusion/Discussion: Deploying AI-based IA in clinical pathology demands more than technical performance - it requires validated workflows, locked algorithm settings, continuous QC, and integration of regulatory standards. This talk equips pathologists and laboratory professionals with a practical, regulation-aligned framework to implement IA assays responsibly and sustainably.

Learning Objectives:

1. Describe the key steps required to develop and analytically validate a digital image analysis assay for clinical use
2. Apply CAP DIA checklist and CLIA requirements to the validation of AI-based pathology image analysis algorithms
3. Identify quality control strategies to monitor image analysis algorithm performance after clinical deployment