Clinical performance of automated machine learning: A systematic review.

Journal: Annals of the Academy of Medicine, Singapore
Published Date: Mar 27, 2024

Abstract

INTRODUCTION: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other.

Authors

  • Arun James Thirunavukarasu
    University of Cambridge School of Clinical Medicine Cambridge UK.
  • Kabilan Elangovan
    Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore.
  • Laura Gutierrez
    Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore.
  • Refaat Hassan
    University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
  • Yong Li
    Department of Surgical Sciences, Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, MI, United States.
  • Ting Fang Tan
    Singapore National Eye Center, Singapore Eye Research Institute Singapore Health Service Singapore Singapore.
  • Haoran Cheng
    Artificial Intelligence and Digital Innovation, Singapore Eye Research Institute, Singapore National Eye Center, Singapore Health Service, Singapore, Singapore; Rollins School of Public Health, Emory University, Atlanta, GA, USA; Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
  • Zhen Ling Teo
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
  • Gilbert Lim
    School of Computing, National University of Singapore.
  • Daniel Shu Wei Ting
    Singapore National Eye Center, Singapore Eye Research Institute Singapore Health Service Singapore Singapore.

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