The diagnosis of hematological disorders is currently established from the combined results of different tests,including those assessing morphology(M),immunophenotype(I),cytogenetics(C),and molecular biology(M)(collec...The diagnosis of hematological disorders is currently established from the combined results of different tests,including those assessing morphology(M),immunophenotype(I),cytogenetics(C),and molecular biology(M)(collectively known as the MICM classification).In this workflow,most of the results are interpreted manually(i.e.,by a human,without automation),which is expertise-dependent,la-bor-intensive,time-consuming,and with inherent interobserver variability.Also,with advances in instru-ments and technologies,the data is gaining higher dimensionality and throughput,making additional challenges for manual analysis.Recently,artificial intelligence(AI)has emerged as a promising tool in clinical hematology to ensure timely diagnosis,precise risk stratification,and treatment success.In this review,we summarize the current advances,limitations,and challenges of AI models and raise potential strategies for improving their performance in each sector of the MICM pipeline.Finally,we share per-spectives,highlight future directions,and call for extensive interdisciplinary cooperation to perfect AI with wise human-level strategies and promote its integration into the clinical workflow.展开更多
基金supported by research funding provided by the National Cancer Institute(U01CA252965,USA)the Eunice Kennedy Shriver National Institute of Child Health and Human Development(R01HD090927 and R01HD103511,USA)+4 种基金the National Institute of Allergy and Infectious Diseases(R01AI144168,R01AI175618,R01AI173021,R01AI174964,R01AI177986,and R01AI179714,USA)the U.S.Department of Defense(W8IXWH1910926,USA)the National Institute of Neurological Disorders and Stroke(R21NS130542,USA)the Science and Technology Project of Sichuan Province of China(Grant No.2023NSFSC1484,China)the 1·3·5 Project for Disciplines of Excellence,West China Hospital,Sichuan Uni-versity(Grant Nos.2023HXFH034 and 25HXJS035,China).
文摘The diagnosis of hematological disorders is currently established from the combined results of different tests,including those assessing morphology(M),immunophenotype(I),cytogenetics(C),and molecular biology(M)(collectively known as the MICM classification).In this workflow,most of the results are interpreted manually(i.e.,by a human,without automation),which is expertise-dependent,la-bor-intensive,time-consuming,and with inherent interobserver variability.Also,with advances in instru-ments and technologies,the data is gaining higher dimensionality and throughput,making additional challenges for manual analysis.Recently,artificial intelligence(AI)has emerged as a promising tool in clinical hematology to ensure timely diagnosis,precise risk stratification,and treatment success.In this review,we summarize the current advances,limitations,and challenges of AI models and raise potential strategies for improving their performance in each sector of the MICM pipeline.Finally,we share per-spectives,highlight future directions,and call for extensive interdisciplinary cooperation to perfect AI with wise human-level strategies and promote its integration into the clinical workflow.
