Exploring the potential of AI-driven brain–computer interfaces for Alzheimer’s disease: an integrated framework for diagnosis and therapeutic intervention

Early diagnosis and timely intervention are critical in Alzheimer's disease (AD), yet conventional assessments fail to capture longitudinal neural dynamics. Although traditional biomarker frameworks offer essential pathological insights, they remain constrained by high costs and restricted accessibility. Recent advances in artificial intelligence (AI) and brain–computer interfaces (BCIs) suggest a potential neurotechnology framework that integrates neural decoding and state-dependent modulation of neural activity to overcome these barriers. This review synthesizes current progress in AI-enhanced BCIs for AD and presents a conceptual BCI system framework in which multimodal neural data are integrated and computationally analyzed to estimate individual disease status and support personalized diagnostic and therapeutic decision-making across the AD continuum. Within this conceptual framework, we first review current neurophysiological approaches for early detection of AD and discuss their potential as enabling technologies for future BCI systems. We then examine therapeutic BCI strategies based on neurofeedback and neuromodulation while clearly distinguishing evidence demonstrated in AD from proof-of-concept findings derived from other neurological disorders. Building on these diagnostic and therapeutic advances, we discuss how AI may enhance neural decoding, adaptive control, and personalized intervention within BCI systems. Finally, we critically evaluate major methodological and translational challenges, including data scarcity, disease heterogeneity, limited model interpretability, and closed-loop implementation, and outline priorities for future clinical translation, including standardized multimodal AD cohorts, biologically grounded explainable AI (XAI), regulatory-aligned system integration, and rigorous prospective clinical validation. This review proposes AI-driven BCIs as a conceptual framework linking early detection, longitudinal monitoring, and circuit-level intervention across the AD continuum. By integrating current evidence on neurophysiological diagnostics, therapeutic BCI strategies, AI-assisted neural decoding, and translational considerations, this review provides a conceptual foundation for the future development and responsible clinical translation of AI-enhanced BCI systems for AD.