TY - JOUR

T1 - Artificial intelligence models in chronic lymphocytic leukemia–recommendations toward state-of-the-art

AU - Agius, Rudi

AU - Parviz, Mehdi

AU - Niemann, Carsten Utoft

N1 - Publisher Copyright: © 2021 Informa UK Limited, trading as Taylor & Francis Group.

PY - 2022

Y1 - 2022

N2 - Artificial intelligence (AI), machine learning and predictive modeling are becoming enabling technologies in many day-to-day applications. Translation of these advances to the patient’s bedside for AI assisted interventions is not yet the norm. With specific emphasis on CLL, here, we review the progress of prognostic models in hematology and highlight sources of stagnation that may be limiting significant improvements in prognostication in the near future. We discuss issues related to performance, trust, modeling simplicity, and prognostic marker robustness and find that the major limiting factor in progressing toward state-of-the-art prognostication within the hematological community, is not the lack of able AI algorithms but rather, the lack of their adoption. Current models in CLL still deal with the ‘average’ patient while the use of patient-centric approaches remains absent. Using lessons from research areas where machine learning has become an enabling technology, we derive recommendations and propose methods for achieving state-of-the-art predictions in modeling health data, that can be readily adopted by the CLL modeling community.

AB - Artificial intelligence (AI), machine learning and predictive modeling are becoming enabling technologies in many day-to-day applications. Translation of these advances to the patient’s bedside for AI assisted interventions is not yet the norm. With specific emphasis on CLL, here, we review the progress of prognostic models in hematology and highlight sources of stagnation that may be limiting significant improvements in prognostication in the near future. We discuss issues related to performance, trust, modeling simplicity, and prognostic marker robustness and find that the major limiting factor in progressing toward state-of-the-art prognostication within the hematological community, is not the lack of able AI algorithms but rather, the lack of their adoption. Current models in CLL still deal with the ‘average’ patient while the use of patient-centric approaches remains absent. Using lessons from research areas where machine learning has become an enabling technology, we derive recommendations and propose methods for achieving state-of-the-art predictions in modeling health data, that can be readily adopted by the CLL modeling community.

KW - artificial intelligence models

KW - chronic lymphocytic leukemia

KW - CLL

KW - guidelines

KW - model

KW - treatment

U2 - 10.1080/10428194.2021.1973672

DO - 10.1080/10428194.2021.1973672

M3 - Review

C2 - 34612160

AN - SCOPUS:85116432894

VL - 63

SP - 265

EP - 278

JO - Leukemia and Lymphoma

JF - Leukemia and Lymphoma

SN - 1042-8194

IS - 2

ER -