TL;DR
Current knowledge on the effectiveness of AI models is systematized, pointing to their key role in building sustainable and pro-social healthcare for the future.
This review synthesizes recent advances in AI-based AD diagnosis, tracing the evolution from traditional ML to DL and LLMs and particular emphasis is placed on the emerging role of LLMs in extracting disease-related information from speech and clinical narratives, integrating heterogeneous biomedical data sources, and...
Recent progress in AI-driven approaches for AD detection is discussed and the importance of interdisciplinary strategies to facilitate earlier diagnosis, improve patient outcomes, and support more effective management of Alzheimer's disease is underscored.
Multimodal fusion architectures, particularly cross-attention transformers integrating motor, voice, and gait data, consistently outperformed unimodal approaches by 4%-8% in the area under the receiver operating characteristic curve (AUC), with the largest gains in early stage detection and differential diagnosis tasks...
A comparative review discusses recent original studies that have investigated the potential of AI-powered approaches in these three modalities for early detection of AD and discusses the challenges that need to be overcome for these techniques to translate into routine clinical practice.
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