Ten quick tips for ensuring machine learning model validity

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...

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Bibliographic Details
Published in:PLoS computational biology 2024-09, Vol.20 (9), p.e1012402
Main Authors: Goh, Wilson Wen Bin, Kabir, Mohammad Neamul, Yoo, Sehwan, Wong, Limsoon
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial Intelligence
Biology and Life Sciences
Computational Biology - methods
Computer and Information Sciences
Education
Evaluation
Humans
Machine Learning
Methodology
Reproducibility of Results
Research and Analysis Methods
Research validity
Social Sciences
Technology application
Citations: Items that this one cites
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1012402