Journal of Applied Hematology · Published 2026-04-01 · DOI 10.4103/joah.joah_33_26
BACKGROUND: Artificial intelligence (AI) is increasingly embedded in hematology diagnostic laboratories, where it supports blood smear interpretation, flow cytometry, genomic analysis, and clinical decision support. While these tools can enhance efficiency and diagnostic accuracy, their deployment raises unresolved questions about clinical safety and legal accountability. This analytical review synthesizes the recent evidence from hematology, clinical risk management, and patient safety literature to map the principal risks, failure modes, and medicolegal challenges associated with AI in hematology diagnostic laboratories. OBJECTIVE: To synthesize recent evidence on the principal risks, failure modes, budgeting for implementation and medicolegal challenges posed by AI in hematology diagnostic laboratories and to propose an actionable safety and accountability agenda for safe clinical integration. METHODOLOGY: After reviewing over 70 articles, 48 were selected for further consideration. The review, which focused on AI applications, safety, risk management, regulatory guidance, and medico-legal analyses pertinent to hematology diagnostic laboratories, was founded on a literature search in the PubMed, Embase, Google Scholar, and Scopus databases. REVIEW: AI is being used in laboratory hematology for flow cytometry, genomic analysis, clinical decision support, and the interpretation of peripheral blood and bone marrow smears. Dataset shift and sampling bias, preanalytical variability and label noise, algorithmic bias and spurious correlations, limited domain adaptability and performance drift, opaque models with untrustworthy explanations, and hallucinated outputs are some of the major technical failure modes that have been identified. Poor workflow integration, clinician overload and alert burden, insufficient training and audit trails, lack of MLDevOps/version control, and inadequate postmarket surveillance are the examples of organizational vulnerabilities. These risks can lead to clinically significant errors (such as missed blasts, unreported acute promyelocytic leukemia or sepsis, biased anemia, or malignancy predictions) and make it more difficult for clinicians, labs, vendors, and institutions to assign blame. Regulatory clearance by itself does not eliminate the requirement for lifecycle monitoring, local validation, and open, human oversight, and equity; economic adoption requires budgeting for acquisition, integration, validation, monitoring, training, and legal safeguards. CONCLUSION: AI promises efficiency and standardization in hematology but, without multicenter validation, continuous bias/drift surveillance, explainable and scope-limited interfaces, enforceable auditability/versioning, MLDevOps governance, mandated human-in-the-loop controls, staff education, clear patient communication, and robust vendor contracts, its deployment may introduce new safety risks and increase medicolegal exposure.
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