Why implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis.

Journal: EBioMedicine
PMID: 39721215

Abstract

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) algorithms have shown great promise in clinical medicine. Despite the increasing number of published algorithms, most remain unvalidated in real-world clinical settings. This study aims to simulate the practical implementation challenges of a recently developed ML algorithm, AI-PAL, designed for the diagnosis of acute leukaemia and report on its performance.

Authors

  • Gernot Pucher
    Department of Haematology & Stem Cell Transplantation, West German Cancer Center, University Hospital Essen, Essen, Germany; Laboratory for Clinical Research and Real-World Evidence, Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
  • Till Rostalski
    Laboratory for Clinical Research and Real-World Evidence, Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
  • Felix Nensa
    Institute for AI in Medicine (IKIM), University Hospital Essen, 45131 Essen, Germany.
  • Jens Kleesiek
    AG Computational Radiology, Abteilung Radiologie, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland. j.kleesiek@dkfz-heidelberg.de.
  • Hans Christian Reinhardt
    Department of Haematology & Stem Cell Transplantation, West German Cancer Center, University Hospital Essen, Essen, Germany.
  • Christopher Martin Sauer
    Department of Haematology & Stem Cell Transplantation, West German Cancer Center, University Hospital Essen, Essen, Germany; Laboratory for Clinical Research and Real-World Evidence, Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany. Electronic address: christopher.sauer@uk-essen.de.

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