Clarifying Validation
Clarifying Validation: Collaborative Approach to a Consistent Terminology
This project is addressing the inconsistent use of the term "validation" across disciplines. We are compiling relevant publications and aim to create a concise resource to clarify terminology and support a peer-reviewed publication on the topic.
We will advocate for a more harmonized, context-aware approach to the term ’validation’, particularly considering the growing influence of AI/ML technologies in diagnostics.
The aim of the paper will be to raise awareness of the inconsistent and context-dependent use of the word, promote collaboration for better definitions, and ensure clearer, more consistent performance metrics in laboratory practice and related fields.
Please send any relevant publications ahead of this meeting.
We look forward to this discussion.
Overview of the Term
Based on the literature review and supplementary files, the term "validation" has been used inconsistently across disciplines, particularly in the context of AI/ML, medical devices, pharmaceuticals, and regulatory science. An overview of different definitions of validation across various fields can be found here.
Meeting #1
This meeting (01/06/2025) addressed the inconsistent use of the term "validation" across subspecialties and disciplines. We discussed the compilation of relevant publications and creation of a resource and in support of a peer-reviewed publication.
Key Takeaways
1. No universal definition of validation exists across disciplines; it varies by field, objective, and regulatory context.
2. AI validation involves both technical (accuracy, robustness) and regulatory (safety, ethics) considerations.
3. Validation is critical in healthcare, pharmaceuticals, engineering, and AI governance to ensure safety, reliability, and compliance.
4. Ongoing efforts (e.g., TRIPOD+AI 2024, British Standard BS30440, FDA AI Validation Guidelines) aim to standardize AI validation approaches.
