Challenges in standardizing preimplantation kidney biopsy assessments and the potential of AI-Driven solutions

This review explores the variability in preimplantation kidney biopsy processing methods, emphasizing their impact on histological interpretation and allocation decisions driven by biopsy findings. With the increasing use of artificial intelligence (AI) in digital pathology, it is timely to evaluate whether these advancements can overcome current challenges and improve organ allocation amidst a growing organ shortage.

Significant inconsistencies exist in biopsy methodologies, including core versus wedge sampling, frozen versus paraffin-embedded processing, and variability in pathologist expertise. These differences complicate study comparisons and limit the reproducibility of histological assessments. Emerging AI-driven tools and digital pathology show potential for standardizing assessments, enhancing reproducibility, and reducing dependence on expert pathologists. However, few studies have validated their clinical utility or demonstrated their predictive performance for long-term outcomes.

Novel AI-driven tools hold promise for improving the standardization and accuracy of preimplantation kidney biopsy assessments. However, their clinical application remains limited due to a lack of proven associations with posttransplant outcomes and insufficient evaluation of predictive performance metrics. Future research should prioritize longitudinal studies using large-scale datasets, rigorous validation, and comprehensive assessments of predictive performance for both short- and long-term outcomes to fully establish their clinical utility.