Lanthanide-doped nanoparticles (LNPs) offer an emerging non-invasive theranostic platform for monitoring and treating neurodegenerative diseases (NDs) own to their high optical stability, deep tissue penetration, and excellent biocompatibility. Their key advantage lies in the ability to produce upconversion or downshifting luminescence under near-infrared excitation, enabling high-resolution deep-tissue imaging and in situ monitoring, particularly suitable for tracking pathological progression and studying molecular interactions in disorders such as Alzheimer’s disease and Parkinson’s disease. By integrating functional components such as organic dyes, noble metal nanoparticles, or therapeutic agents, LNPs can be engineered into multifunctional theranostic nanoplatforms capable of simultaneous diagnosis and targeted therapy. Moreover, their precisely tunable emission properties open new avenues for deep-brain imaging and optical modulation. This review systematically summarizes the luminescence mechanisms of LNPs and recent advances in their applications for biosensing and diagnosis in NDs. It covers the detection of key biomarkers, including metal ions, nucleic acids, proteins, and reactive oxygen species. The discussion further extends to the therapeutic strategies targeting intracellular and microenvironmental factors, as well as synergistic approaches, with a particular emphasis on the role of LNPs in targeted drug delivery and combined theranostics. Finally, the review discusses future prospects for leveraging this platform to improve clinical outcomes in NDs.

Wearable electrocardiography (ECG) is shifting cardiovascular monitoring from episodic in-hospital testing to continuous out-of-hospital assessment. Artificial intelligence-enabled ECG (AI-ECG) provides a new pathway for extracting clinically useful information from out-of-hospital wearable recordings. This review is organized around the wearable AI-ECG monitoring pipeline, summarizing key advances in model development, clinical application, and real-world validation, while emphasizing the special requirements that wearable scenarios impose on algorithm design and clinical translation. At present, arrhythmia screening remains the most mature application of AI-ECG, with deep learning models achieving cardiologist-level or clinician-comparable performance in several well-defined tasks. With the growing availability of long-term continuous recordings, research is further extending from post-event recognition to pre-event warning, particularly for high-risk events such as acute atrial fibrillation and malignant ventricular arrhythmias. Related methods are evolving from traditional feature-based machine learning toward deep learning and foundation models that can exploit waveform morphology, rhythm dynamics, and long-range temporal information. Beyond rhythm disorders, AI-ECG is also being explored for structural cardiac abnormalities, metabolic disorders, and broader systemic risk prediction, suggesting a potential role for ECG as a digital biomarker platform. However, several barriers continue to limit clinical translation, including limited cross-device and cross-population generalizability, insufficient interpretability, and the lack of prospective real-world validation. Future progress will likely depend on standardized data systems, artifact-aware modeling, cross-device validation, foundation models, longitudinal risk modeling, and intelligent systems designed for clinical workflows. Overall, wearable AI-ECG is evolving from passive abnormality detection toward continuous, proactive, and personalized health risk assessment.