Why traditional validation may fall short for artificial intelligence in bioanalysis: a perspective from the European...

Area of Science:

• Bioanalysis
• Artificial Intelligence
• Regulatory Science

Background:

• Traditional validation frameworks are inadequate for artificial intelligence (AI) in bioanalysis.
• AI systems are dynamic and require different qualification approaches than static, deterministic tools.
• The European Bioanalysis Forum 2025 Spring Focus Workshop highlighted these challenges.

Purpose of the Study:

• To challenge the assumption that AI applications should be validated using conventional methods.
• To propose adaptive qualification as a new framework for AI in bioanalysis.
• To explore the evolution of scientific oversight for AI systems.

Main Methods:

• Conceptual framework development based on workshop discussions.
• Analysis of the limitations of current validation practices for AI.
• Proposal of adaptive qualification principles: scientific oversight, contextual relevance, and earned trust.

Main Results:

• AI should be viewed as a learning system, akin to a trainee, rather than a static tool.
• Oversight must evolve beyond mere compliance to ensure transparency, robustness, and fitness for purpose.
• Adaptive qualification offers a path forward for validating AI in bioanalysis.

Conclusions:

• Scientists must lead the adaptation of validation processes for AI.
• The focus should be on guiding innovation responsibly with clarity and collaboration.
• Maintaining patient focus is paramount in the evolving landscape of AI in bioanalysis.