Artificial intelligence in hematological diagnostics: Game changer or gadget?

被引：29

作者：

Walter, Wencke ^{[1
]}

Pohlkamp, Christian ^{[1
]}

Meggendorfer, Manja ^{[1
]}

Nadarajah, Niroshan ^{[1
]}

Kern, Wolfgang ^{[1
]}

Haferlach, Claudia ^{[1
]}

Haferlach, Torsten ^{[1
]}

机构：

[1] MLL Munich Leukemia Lab, Max Lebsche Pl 31, D-81377 Munich, Germany

来源：

BLOOD REVIEWS | 2023年 / 58卷

关键词：

Artificial intelligence; Precision medicine; Transfer learning; Machine learning; Hematological diagnostics; HEALTH-ORGANIZATION CLASSIFICATION; LYMPHOID-CELLS; 5TH EDITION; MEDICINE; BLOOD; AI; TRANSFORM; NETWORKS; CARE;

D O I：

10.1016/j.blre.2022.101019

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

The future of clinical diagnosis and treatment of hematologic diseases will inevitably involve the integration of artificial intelligence (AI)-based systems into routine practice to support the hematologists' decision making. Several studies have shown that AI-based models can already be used to automatically differentiate cells, reliably detect malignant cell populations, support chromosome banding analysis, and interpret clinical variants, contributing to early disease detection and prognosis. However, even the best tool can become useless if it is misapplied or the results are misinterpreted. Therefore, in order to comprehensively judge and correctly apply newly developed AI-based systems, the hematologist must have a basic understanding of the general concepts of machine learning. In this review, we provide the hematologist with a comprehensive overview of various ma-chine learning techniques, their current implementations and approaches in different diagnostic subfields (e.g., cytogenetics, molecular genetics), and the limitations and unresolved challenges of the systems.

引用

页数：11

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