Latest AI and machine learning research in geriatrics for healthcare professionals.
Spoken-language analysis via prompt-based domain-adaptive models is a promising direction for low-resource, non-invasive dementia screening, but such models remain internally opaque. We study the interpretability of the Domain-Adapted models via Prompt-based Fine-tuning (DAPF) framework, which casts dementia detection as diagnosis-related masked-token prediction. We interpret DAPF and strong basel...
Aging is a progressive decline in biological function that is proposed to be driven by the accumulation of epigenetic noise and the loss of epigenetic information. Among epigenetic readouts, DNA methylation has been extensively used to develop aging clocks, machine learning models that predict age from molecular data. However, DNA methylation clocks are relatively difficult to interpret and remain...
Background and Objectives: Word-finding difficulty is common in healthy aging and in neurologic disorders, including temporal lobe epilepsy (TLE) and ...
Purpose: To quantify how evaluation annotations influence measured pulmonary embolism (PE) segmentation performance relative to model training changes...
Video generation is progressing beyond isolated clips toward long-form narratives and interactive worlds, requiring models to preserve identities, fol...
Despite their remarkable success in modeling complex data, generative models face a fundamental tradeoff. Global approaches can capture full structura...
Purpose Prevention and early detection of osteoporosis remains a global challenge, more so in regions like the Philippines where screening barriers ex...
Background Glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT2) inhibitors are increasingly used for type 2 di...
In Alzheimer's disease (AD), misfolded proteins emerge across the entire brain in structured, yet not rigid, spatiotemporal patterns. Yet, a systemati...
Abstract EEG foundation models (EEG-FMs) are evaluated almost entirely on disease-discrimination accuracy. A clinical biomarker additionally requires ...
Brain-age models derived from diffusion MRI-based structural connectomes may provide imaging biomarkers of accelerated brain aging, but their biologic...
Full-length RNAs, particularly messenger RNAs, often exceed the context lengths used to pretrain existing RNA foundation models, limiting complete-tra...
Video generation is progressing beyond isolated clips toward long-form narratives and interactive worlds, requiring models to preserve identities, fol...
EEG microstates are a distinct number of quasi-stable spatial distributions of brain activity. Microstate trajectories are strongly suspected to refle...
Document processing pipelines traditionally cascade optical character recognition (OCR) engines with downstream models for structured information extr...
Reliable spatial understanding is an important prerequisite for future medical vision-language systems that aim to support radiological report generat...
Background and Objectives: Subjective cognitive decline (SCD), self-reported worsening confusion or memory over the past year, is a common early marke...
Epigenetic clocks based on DNA methylation patterns are among the most accurate molecular correlates of chronological age, yet widely used clocks are ...
Explainability methods applied to deep learning models for Alzheimer's disease neuroimaging produce attribution maps that vary substantially across me...
Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remai...