Ten quick tips for ensuring machine learning model validity.

Journal: PLoS computational biology
Published Date: Sep 19, 2024

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) models are increasingly deployed on biomedical and health data to shed insights on biological mechanism, predict disease outcomes, and support clinical decision-making. However, ensuring model validity is challenging. The 10 quick tips described here discuss useful practices on how to check AI/ML models from 2 perspectives-the user and the developer.

Authors

  • Wilson Wen Bin Goh
    School of Biological Sciences, Nanyang Technological University, Singapore 637551, Republic of Singapore. Electronic address: wilsongoh@ntu.edu.sg.
  • Mohammad Neamul Kabir
    Department of Computer Science, National University of Singapore, 13 Computing Drive, 117417, Singapore, Singapore. neamul@u.nus.edu.
  • Sehwan Yoo
    Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
  • Limsoon Wong
    Department of Computer Science, National University of Singapore, Singapore; Department of Pathology, National University of Singapore, Singapore.

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