arXiv:2611.08188v1 Announce Type: new Abstract: Objective: This review aims to provide a comprehensive synthesis of domain adaptation (DA) and domain general- ization (DG) strategies for AD detection. It focuses on their potential to improve the robustness, generalizability, and clinical reliability of ML-based diagnostic systems. Methods: We conduct a comprehensive scoping review of existing DA and DG approaches across multiple data modalities. The reviewed studies are organized according to data modality, algorithmic framework, adaptation strat- egy, clinical relevance, and reported cross-domain performance. Key methodological trends, performance gains, limitations, and remaining challenges are analyzed to identify future research directions. Results: DA and DG approaches have demonstrated encouraging improvements in cross-domain performance and model robustness. Emerging modalities, such as speech-based approaches, EEG and eye-tracking data, show great po- tential for early, accessible, and non-invasive cognitive assessments. However, important limitations remain, including insufficient external validation, limited interpretability, computational complexity, privacy concerns, and challenges in scaling to routine clinical practice. Conclusion: DA and DG provide important methodological pathways for developing more generalizable ML-based systems for AD detection. Future research should prioritize explainable, computationally efficient, privacy-preserving, and clinically validated DA/DG techniques. Strengthening these aspects will be essential for translating robust AI- based AD detection tools into clinically deployable solutions.