A Generalized Responsible AI Framework for Trustworthy Clinical Prediction: Explainability, Fairness, Performance,...

Objectives: Alzheimer's disease (AD) remains one of the most prevalent neurodegenerative conditions among older adults, underscoring the urgent need for accurate and ethically grounded early detection methods. Artificial intelligence (AI) techniques, particularly machine learning and deep learning models, show promise in leveraging neuroimaging biomarkers to support early diagnosis. However, significant challenges persist regarding model explainability, accountability, and responsible implementation in real-world healthcare settings. This study presents a generalized Responsible AI (RAI) framework composed of four core components-explainability, fairness, predictive performance, and uncertainty quantification-to address these challenges. Method: Using the TADPOLE neuroimaging dataset, we implemented a Feedforward Neural Network (FNN) within a unified Responsible AI (RAI) framework integrating explainability, fairness, predictive performance, and uncertainty quantification. Although Random Forest achieved slightly higher predictive accuracy (95%), the FNN was selected as the primary model because it better supports end-to-end uncertainty estimation through Monte Carlo Dropout, enabling more reliable clinical decision support. Results: The proposed framework demonstrated strong predictive performance (92% accuracy), improved fairness reflected by an equalized odds difference of 0.124, and progressively lower predictive entropy across training iterations, indicating enhanced confidence in predictions. The framework further enabled model transparency through explainability analyses and supported the identification of low-confidence predictions for potential clinical review. Conclusions: Our findings highlight not only the feasibility of integrating RAI principles into AD prediction pipelines but also the persistent challenges of applying such frameworks to real-world clinical data. This work contributes practical insights toward operationalizing Responsible AI in healthcare contexts.