Multimodal AI for Alzheimer’s Disease Diagnosis: A Systematic Review of Datasets, Models and Modalities (Preprint)

BACKGROUND Alzheimer’s disease (AD) is a major global health challenge, characterised by progressive cognitive decline and rising prevalence due to population ageing. Despite increased awareness, a substantial diagnostic gap remains, underscoring the urgent need for scalable and accurate early detection methods. Artificial intelligence (AI) provides a promising solution by integrating multimodal data to enhance diagnostic accuracy and clinical decision-making. However, current research remains fragmented, with studies often focusing separately on clinical phenotyping or linguistic-based cognitive impairment data. OBJECTIVE This review aims to systematically synthesise recent advances in multimodal AI approaches for AD diagnosis and risk prediction, bridging the gap between clinical phenotyping and linguistic-based cognitive assessment research. METHODS Following the PRISMA 2020 guidelines, a comprehensive search was conducted across major databases to identify studies published in the past five years that applied multimodal AI models for AD diagnosis or cognitive impairment detection. A total of 97 eligible studies were included for qualitative synthesis. Extracted data covered modality combinations, AI modelling strategies, evaluation metrics, and dataset usage. RESULTS Multimodal AI models consistently outperformed single-modality approaches in diagnostic accuracy and generalisability. Deep learning and ensemble learning frameworks were the most frequently adopted methods, offering strong feature-fusion capabilities. However, methodological heterogeneity, data imbalance, and limited dataset accessibility were recurring challenges. The review further summarises representative modality pairings such as imaging + clinical, imaging + genetic, and speech + cognitive features and provides an overview of publicly available multimodal datasets to facilitate reproducibility and benchmarking. CONCLUSIONS This systematic review provides a comprehensive overview of multimodal AI models for AD diagnosis, uniquely integrating both clinical phenotyping and linguistic-based cognitive assessment perspectives. It highlights key methodological trends, dataset limitations, and opportunities for future research toward standardised, interpretable, and generalisable AI-driven diagnostic frameworks for Alzheimer’s disease.