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Ten quick tips for ensuring machine learning model validity.
Wilson Wen Bin Goh1,2,3,4,5, Mohammad Neamul Kabir1,3, Sehwan Yoo1,3
1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Plos Computational Biology
|September 19, 2024
Summary
This study offers 10 practical tips for validating Artificial Intelligence (AI) and Machine Learning (ML) models in healthcare. It addresses key practices for both users and developers to ensure reliable AI/ML model performance.
Area of Science:
- Biomedical informatics
- Health data science
- Artificial intelligence in medicine
Background:
- Artificial Intelligence (AI) and Machine Learning (ML) are vital tools for analyzing biomedical and health data.
- These models offer insights into biological mechanisms, disease prediction, and clinical decision support.
- Ensuring the validity and reliability of these AI/ML models is a significant challenge in the field.
Purpose of the Study:
- To provide practical guidance on validating AI/ML models used in biomedical and health applications.
- To present 10 actionable tips for checking the integrity and performance of AI/ML models.
- To address the validation process from both user and developer perspectives.
Main Methods:
- The study outlines a set of 10 quick tips for model validation.
- Practices are discussed considering the viewpoints of AI/ML model users.
- Practices are also discussed considering the viewpoints of AI/ML model developers.
Main Results:
- The 10 tips cover essential checks for AI/ML model validity.
- The recommendations aim to enhance the trustworthiness of AI/ML models in healthcare settings.
- The guidance facilitates a more rigorous approach to AI/ML model deployment.
Conclusions:
- Implementing these validation practices is crucial for the responsible use of AI/ML in healthcare.
- A dual perspective (user and developer) is essential for comprehensive model validation.
- These tips contribute to building confidence in AI/ML applications for biological and clinical insights.
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