Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders

Advances in digital pathology have allowed a number of opportunities such as decision support using artificial intelligence (AI). The application of AI to digital pathology data shows promise as an aid for pathologists in the diagnosis of haematological disorders. AI-based applications have embraced benign haematology, diagnosing leukaemia and lymphoma, as well as ancillary testing modalities including flow cytometry. In this review, we highlight the progress made to date in machine learning applications in haematopathology, summarise important studies in this field, and highlight key limitations. We further present our outlook on the future direction and trends for AI to support diagnostic decisions in haematopathology.

Automated summary

Advances in digital pathology have provided opportunities for decision support using artificial intelligence (AI), with AI applications showing promise in the diagnosis of haematological disorders. AI-based applications in haematopathology have already covered the diagnosis of leukemia, lymphoma, and ancillary testing modalities like flow cytometry.