The Future of Lymphoma Pathology: How AI Is Reshaping Diagnosis

Artificial intelligence (AI) is rapidly transforming lymphoma pathology, offering new opportunities to enhance diagnostic accuracy, efficiency, and reproducibility. Recent advances presented at major hematology and oncology meetings highlight the integration of machine learning with digital pathology to improve classification of lymphoma subtypes, particularly in challenging or ambiguous cases. Deep learning algorithms can augment morphologic assessment, quantify tumor microenvironment features, and support biomarker identification, including immunophenotypic and molecular correlates. Emerging data also demonstrate AI’s role in predicting prognosis and therapeutic response through multi-omic data integration. Additionally, AI-enabled workflows may reduce interobserver variability and streamline pathology operations. Key discussions will address validation, regulatory considerations, and real-world implementation, as well as the evolving collaboration between pathologists and AI tools in precision lymphoma diagnosis.