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 here discuss useful practices on how to check AI/ML models from 2 perspectives-the user and the developer.
Authors
- Mohammad Neamul Kabir (ORCID: https://orcid.org/0000-0002-3616-896X)
- Limsoon Wong (ORCID: https://orcid.org/0000-0003-1241-5441)
- Wilson Wen Bin Goh (ORCID: https://orcid.org/0000-0003-3863-7501)
- Sehwan Yoo
Institutions
- National University of Singapore (SG)
- Nanyang Technological University (SG)
- Imperial College London (GB)
Publication Details
- Journal
- PLoS Computational Biology
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1371/journal.pcbi.1012402
- Citations
- 2
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- Ministry of Education