Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), has been demonstrated to be able to activate complement, making patients with deficiency in negative complement regulation, such as paroxysmal nocturnal hemoglobinuria (PNH), particularly vulnerable to complement-mediated cell damage. We report a case of a patient who presented with fatigue, facial swelling, and upper respiratory infection (URI) symptoms and was found to have COVID-19 with laboratory tests showing severe hemolysis and pancytopenia secondary to PNH.

Idiopathic multicentric Castleman disease (iMCD) is a rare cytokine‐driven disorder characterized by systemic inflammation, organ dysfunction, and altered lymph node microscopic architecture. Over the past decade, diagnostic criteria have evolved significantly, integrating clinical, histopathological, and molecular biomarker advancements. Key drivers of iMCD pathogenesis, such as interleukin‐6 dysregulation and other dysfunctional cytokine signaling, have been identified and led to the development of targeted therapies like siltuximab and tocilizumab. Histopathologic refinements have highlighted distinct subtypes, such as hypervascular, plasmacytic, and mixed histologic patterns, while molecular discoveries have unveiled potential paraneoplastic and clonal processes. Emerging technologies, including single‐cell sequencing, spatial transcriptomics, and digital pathology, offer promise in refining diagnostic precision and advancing personalized medicine. This review synthesizes historical frameworks, recent breakthroughs, and future directions in iMCD diagnostics, emphasizing the importance of multidisciplinary approaches for improved patient outcomes.

Background and Objectives: Diagnostics for hematologic diseases rely on integrated assessment of clinical manifestation, morphology, flow cytometry, and molecular testing. Current classification systems, including the WHO HAEM5 and the International Consensus Classification, highlight the central role of genomics in defining disease entities and risk. Simultaneously, laboratories face growing case complexity and staffing challenges. Automation, collaborative robots (cobots), digital morphology platforms, and artificial intelligence (AI) have begun to address these issues. Here we examine the application of these technologies in hematopathology and molecular diagnostics and consider their translational potential to improve diagnostic accuracy and, ultimately, patient care. Methods: A review of peer-reviewed literature and technical reports published through December 2025 was performed, focusing on digital morphology platforms, AI for peripheral blood and marrow interpretation, AI-enabled flow cytometry, automated and robotic deployments in clinical laboratories, and machine learning applications in molecular hematopathology. Results: Digital morphology analyzers show strong concordance with manual microscopy and now serve as efficient platforms for AI-assisted differentials, cell classification, and fibrosis quantification. Deep learning applied to multiparameter flow cytometry achieves performance comparable to expert review in distinguishing mature B-cell neoplasms and acute leukemias. Automated solutions, cobot systems and robotic-arm-based slide-scanning clusters have demonstrated substantial gains in throughput and pre-analytic consistency. AI models in molecular hematopathology increasingly assist with variant interpretation, genetic risk stratification, and linking morphologic and genomic findings. Conclusions: AI is beginning to change how hematopathology and molecular diagnostics are practiced. Successful translation will depend on disease-specific validation, the development of multi-modal models aligned with ICC and WHO frameworks, and laboratory governance that maintains expert oversight.