Explainable AI for Brain Age Prediction: Design, Implementation, and Formative Evaluation of an Interactive Tool

Brain age prediction is a valuable tool for distinguishing normal and pathological aging, offering quantitative insights into subtle brain structure changes. However, clinical adoption remains limited due to the complexity and low interpretability of Machine Learning (ML) algorithms. Human-Centered Artificial Intelligence (HCAI) and eXplainable AI (XAI) address these challenges by emphasizing user involvement, explainability, and facilitation of clinical applications. By combining advanced ML models with intuitive interfaces and transparent visualizations, HCAI fosters trust and usability in neurological practice. This paper presents the “Brain Age Predictor”, a web-based tool combining a deep learning model for brain age estimation with SHAP-based interpretability techniques and an interactive simulation panel. We conducted a preliminary study involving five neurology residents to assess its usability, interpretability, and potential value in clinical practice. The results show that the Brain Age Predictor effectively supports clinical exploration of brain aging, with participants praising its ease of use and clarity. Feedback highlighted areas for improvement, including richer visualizations, more detailed explanations, and tools for longitudinal patient monitoring.