Artificial intelligence in hematology

Clinical Snapshot

Key Findings

• Effect Size: Not quantified - narrative review format

• Primary Outcome: AI/ML show potential for enhancing diagnostic accuracy, risk stratification, and treatment prediction in hematology

• Nnt Or Sensitivity: Few AI/ML tools fully implemented in clinical practice despite potential benefits

Clinical Application

Limited by data quality issues, infrastructure requirements, and regulatory frameworks Relevant to Australian hematology practice; implementation would require TGA approval for diagnostic tools and consideration of PBS funding for AI-enhanced diagnostics Hematologists, pathologists, laboratory medicine specialists, and healthcare administrators

Abstract

Artificial intelligence (AI) and its subdiscipline, machine learning (ML), have the potential to revolutionize health care, including hematology. The diagnosis and treatment of hematologic disorders depend on the integration of diverse data sources, such as imaging, pathology, omics, and laboratory parameters. The increasing volume and complexity of patient data have made clinical decision-making more challenging. AI/ML hold significant potential for enhancing diagnostic accuracy, risk stratification, and treatment response prediction through advanced modeling techniques. Generative AI, a recent advancement within the broader field of AI, is poised to have a profound impact on health care and hematology. Generative AI can enhance the development of novel therapeutic strategies, improve diagnostic workflows by generating high-fidelity images or pathology reports, and facilitate more personalized approaches to patient management. Its ability to augment clinical decision-making and streamline research represents a significant leap forward in the field. However, despite this potential, few AI/ML tools have been fully implemented in clinical practice due to challenges related to data quality, equity, advanced infrastructure, and the establishment of robust evaluation metrics. Despite its promise, AI implementation in hematology faces critical challenges, including bias, data quality issues, and a lack of regulatory frameworks and safety standards that keep pace with rapid technological advancements. In this review, we provide an overview of the current state of AI/ML in hematology as of 2025, identify existing gaps, and offer insights into future developments.

References

1. 1.Nazha, A., Elemento, O., Ahuja, S., Lam, B., Miles, M., Shouval, R., McWeeney, S., Sirhan, S., Srisuwananukorn, A., & Haferlach, T. (2025). Artificial intelligence in hematology. Blood. https://doi.org/10.1182/blood.2025029876