This review conducts a bibliometric analysis of 431 studies from five major online databases, and provides a scoping review of 86 artificial intelligence (AI) models. Key focuses include motor activity, neurocognitive tests, eye tracking, and speech analysis.
SHADE-AD, a Large Language Model (LLM) framework for Synthesizing Human Activity Datasets Embedded with AD features. Leveraging both public datasets and our own collected data from 99 AD patients, SHADE-AD synthesizes human activity videos that specifically represent AD-related behaviors.
ADMarker offers a new platform
that can allow AD clinicians to characterize and track the
complex correlation between multidimensional interpretable
digital biomarkers, demographic factors of patients, and AD
diagnosis in a longitudinal manner.
DrHouse introduces a novel diagnostic algorithm that concurrently evaluates potential diseases and their likelihood, facilitating more nuanced and informed medical assessments.
Harmony, a new system for heterogeneous multi-modal federated learning. Harmony disentangles the multi-modal network training in a novel two-stage framework, namely modality-wise federated learning and federated fusion learning.
Cosmo can effectively extract both consistent and complementary information across different modalities for efficient fusion by integrating novel fusion-based contrastive learning and quality-guided attention mechanisms.
ClusterFL can efficiently drop out the nodes that converge slower or have little correlation with other nodes in each cluster, significantly speeding up the convergence while maintaining the accuracy performance