XAI-Refine: An Automated Explanation-Knowledge Loop for Brain-Age Prediction

Accuracy alone doesn't guarantee trustworthiness: The paper demonstrates that a brain-age prediction model can make accurate predictions without necessarily using biologically meaningful mechanisms, highlighting why organizations need to look beyond performance metrics when deploying AI in healthcare.

Automated fact-checking of AI explanations improves reliability: XAI-Refine automatically verifies that the features an AI model relies on are supported by existing neuroscience literature, creating a systematic way to validate whether model explanations reflect real scientific knowledge rather than statistical artifacts.

Iterative refinement with safeguards prevents unintended consequences: The system includes "guardrails" that ensure model improvements don't degrade predictive performance and that changes remain focused on the intended target, demonstrating a practical approach to responsible AI development that balances multiple objectives simultaneously.

Reproducibility matters for clinical AI applications: By consolidating explanations across multiple training runs, the method identifies which features the model consistently relies on versus which are unstable, addressing a critical concern for deploying AI in medical settings where consistency and reliability are essential.

Bridging AI and domain expertise requires structured processes: The paper shows how to systematically connect AI explanations with expert knowledge through automated literature verification and formal constraints, providing a replicable framework for ensuring AI systems align with established scientific understanding.