Alzheimer’s disease (AD) is the most common cause of dementia, marked by cognitive decline and memory loss. Recently, machine learning and deep learning techniques have introduced promising solutions for improving AD detection through MRI, especially in settings where specialists may not be readily available. These techniques offer the potential to assist general practitioners and non-specialists in busy clinical environments. However, the ‘black box’ nature of many AI techniques makes it challenging for non-expert physicians to fully trust their diagnostic accuracy. In this review, we critically evaluate current explainable AI (XAI) methods applied to AD detection and highlight their limitations. In addition, a new interpretability framework, called “Feature-Augmented”, was theoretically designed to improve model interpretability. This approach remains underexplored, primarily due to the scarcity of explainable AD-specific datasets. Furthermore, we underscore the importance of AI models being accurate and explainable, which enhance diagnostic confidence and patient care outcomes.

Background/Objectives: This study presents a survey-based evaluation of an explainable AI (Feature-Augmented) approach, which was designed to support the diagnosis of Alzheimer’s disease (AD) by integrating clinical data, MMSE scores, and MRI scans. The approach combines rule-based reasoning and example-based visualization to improve the explainability of AI-generated decisions. Methods: Five doctors participated in the survey: two with 6 to 10 years of experience and three with more than 10 years of experience in the medical field and expertise in AD. The participants evaluated different AI outputs, including clinical feature-based interpretations, MRI-based visual heat maps, and a combined interpretation approach. Results: The model achieved a 100% trust score, with 20% of the participants reporting full trust and 80% expressing conditional trust, understanding the diagnosis but seeking further clarification. Overall, the participants reported that the integrated explanation format improved their understanding of the model decisions and enhanced their confidence in using AI-assisted diagnosis. Conclusions: To our knowledge, this paper is the first to gather the views of medical experts to evaluate the explainability of an AI decision-making model when diagnosing AD. These preliminary findings suggest that explainability plays a key role in building trust and ease of use of AI tools in clinical settings, especially when used by experienced clinicians to support complex diagnoses, such as AD.

Background/Objectives: Artificial intelligence (AI) models for Alzheimer’s disease (AD) diagnosis often face the challenge of limited explainability, hindering their clinical adoption. Previous studies have relied on full-scale MRI, which increases unnecessary features, creating a “black-box” problem in current XAI models. Methods: This study proposes an explainable ensemble-based diagnostic framework trained on both clinical data and mid-slice axial MRI from the ADNI and OASIS datasets. The methodology involves training an ensemble model that integrates Random Forest, Support Vector Machine, XGBoost, and Gradient Boosting classifiers, with meta-logistic regression used for the final decision. The core contribution lies in the exclusive use of mid-slice MRI images, which highlight the lateral ventricles, thus improving the transparency and clinical relevance of the decision-making process. Our mid-slice approach minimizes unnecessary features and enhances model explainability by design. Results: We achieved state-of-the-art diagnostic accuracy: 99% on OASIS and 97.61% on ADNI using clinical data alone; 99.38% on OASIS and 98.62% on ADNI using only mid-slice MRI; and 99% accuracy when combining both modalities. The findings demonstrated significant progress in diagnostic transparency, as the algorithm consistently linked predictions to observed structural changes in the dilated lateral ventricles of the brain, which serve as a clinically reliable biomarker for AD and can be easily verified by medical professionals. Conclusions: This research presents a step toward more transparent AI-driven diagnostics, bridging the gap between accuracy and explainability in XAI.

Background/Objectives: In clinical practice, Explainable AI (XAI) enables non-specialists and general practitioners to make precise diagnoses. Current XAI approaches are limited, as many rely solely on either presenting explanations of clinical data or presenting explanations of MRI, or presenting explanations in unclear ways, reducing their clinical utility. Methods: In this paper, we propose a novel Hybrid Explainable AI (HXAI) framework. This framework uniquely integrates both model-agnostic (SHAP) and model-specific (Grad-CAM) explanation methods within a unified structure for the diagnosis of Alzheimer’s disease. The dual-layer explainability constitutes the main originality of this study, as it provides the possibility of interpreting quantitative (at the feature level) and spatial (at the region level) data within a single diagnostic framework. Clinical features (e.g., Mini-Mental State Examination (MMSE), normalized Whole Brain Volume (nWBV), Socioeconomic Status (SES), age) are combined with MRI-derived features extracted via ResNet50, and these features are integrated using ensemble learning with a logistic regression meta-model. Results: The corresponding validation reflects the explainability accuracy of these feature-based explanations, with removal-based tests achieving 83.61% explainability accuracy, confirming the importance of these features. Model-specific information was used to explain MRI predictions, achieving 58.16% explainability accuracy of visual explanations. Conclusions: Our HXAI framework integrates both model-agnostic and model-specific approaches in a structured manner, supported by quantitative metrics. This dual-layer interpretability enhances transparency, improves explainability accuracy, and provides an accurate and interpretable framework for AD diagnosis, bridging the gap between model accuracy and clinical trust.