Cushing’s syndrome (CS) is a rare endocrine disorder characterized by chronic hypercortisolism, and facial image-based intelligent diagnosis has emerged as a promising non-invasive approach. However, existing diagnostic models suffer from two core bottlenecks: inefficient fusion of deep semantic features and clinical prior features, and insufficient multi-view facial feature disentanglement without CS-specific pathophysiological constraints. To address these limitations, we propose a novel Multi-View Facial Feature Disentanglement Network (MVFFD-Net) for high-precision automatic CS diagnosis. The network takes five standard facial views (frontal, bilateral ${45}^{\circ }$ oblique, and bilateral ${90}^{\circ }$ lateral views) as input, with three key innovations: a bidirectional cross-attention module for synergistic fusion of deep and clinical prior features, a multi-path disentanglement architecture with multi-objective loss for separating view-invariant and view-specific features, and a graph attention fusion framework for adaptive multi-view feature integration. Unlike state-of-the-art models that mainly rely on single-view representations or generic multi-view fusion, MVFFD-Net explicitly integrates clinically guided multimodal fusion, CS-oriented feature disentanglement, and pathology-aware graph-based multi-view aggregation. Experimental results demonstrate that MVFFD-Net achieves an F1-score of 98.78% for CS diagnosis, significantly outperforming representative state-of-the-art methods in the current experimental setting. In this study, the task is defined as subject-level AI-assisted diagnosis to distinguish individuals with CS from matched controls using five standard facial views. Accordingly, the proposed framework is intended to provide non-invasive auxiliary diagnostic support rather than replace standard endocrinological evaluation and biochemical confirmation. More broadly, this clinically informed multi-view learning framework shows promising potential as a non-invasive auxiliary screening tool, though external multi-center validation remains necessary prior to large-scale clinical application.

Alzheimer’s disease (AD) is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function, but the etiology of AD is still unclear. With the explosive growth of electronic health data, the application of artificial intelligence (AI) in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches, and improving the disease’s diagnostic and prognostic outcome. This paper first briefly introduces AI technologies and applications in medicine, and then presents a comprehensive review of AI in AD. In simple, it includes etiology discovery based on genetic data, computer-aided diagnosis (CAD), computer-aided prognosis (CAP) of AD using multi-modality data (genetic, neuroimaging and linguistic data), and pharmacological or non-pharmacological approaches for treating AD. Later, some popular publicly available AD datasets are introduced, which are important for advancing AI technologies in AD analysis. Finally, core research challenges and future research directions are discussed.