Geriatrics

Pedagogical AI in Mental Health: A Tri-Stream Fine-Tuned LLM Framework for Automated Clinical Supervision and Risk Triage

Modern mental healthcare faces a critical shortage of senior supervisory oversight, leading to a "supervision gap" where novice therapists manage high-stakes risks with delayed professional feedback. This paper proposes a new framework utilizing a fine-tuned Mistral-7B-instruct model as an automated "Supervisor-in-the-Loop" system. By leveraging 106 sessions from the DAIC-WOZ dataset, the model pe...

Adaptive Multi-Agent Feature Selection for Personalized Fall Risk Prevention

Falls among older adults represent a major public health challenge driven by complex, time-varying interactions across multiple risk domains. Effective fall risk factor identification requires learning from heterogeneous longitudinal data while accounting for sparse and delayed fall-related outcome events. However, existing approaches are largely static and fail to adaptively model evolving, indiv...

NanoSleep: A Parameter-Efficient Hybrid Temporal Convolutional Network for Single-Channel Sleep Stage Classification

Sleep stage classification from single-channel electroencephalography (EEG) is essential for wearable and home-based sleep monitoring. However, many d...

Delta2Gamma: Band-Wise Adaptive Contrastive Learning of EEG for Alzheimer's Disease Detection

Low-cost, scalable screening for dementia remains an open problem. Imaging-based diagnosis is costly and hard to deploy widely. Electroencephalography...

BrainNorm: A Foundation Model that knows Normal via Semantic Atlas Pretraining

We introduce BrainNorm, a normative foundation model, trained and tested on ~66,000 T1-weighted structural MRI (T1w sMRI) scans. By leveraging languag...

Prospective Validation of a Deep Learning Model to Detect Structural Heart Disease from Apple Watch ECGs: The WATCH-SHD Study

Importance: Consumer wearables such as the Apple Watch can record single-lead electrocardiograms (ECGs) but are used mainly to detect rhythm disorders...

A Comprehensive Benchmark of EEG-Based BCI Deep Learning Models for MCI and Dementia Classification

Electroencephalography (EEG) is a promising tool for automated detection of mild cognitive impairment (MCI) and dementia, but comparisons across studi...

Analysis of gut bacterial communities leveraging AI methods unveils novel relations between species in Alzheimer's disease

The gut microbiome has been increasingly implicated in Alzheimers disease (AD), with studies reporting numerous species- and genus-level differences. ...

End-user perspectives on design and implementation of a novel SkinScan3D (SS3D) device for monitoring Kaposi Sarcoma in East Africa: a qualitative study

Introduction: SkinScan3D (SS3D) is a novel, artificial intelligence-enabled device that provides objective three-dimensional measurements for monitori...

Beyond Simulated Benchmarks: Evaluating Motion Representations for Fall Detection Under Real-World Data Scarcity

Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention...

Bridging Severe Cross-Modal Misalignment: End-to-End Visible-Infrared Object Detection via Explicit Feature-Domain Affine Registration

Visible-infrared object detection relies on complementary RGB and thermal cues, but its performance is often degraded by cross-modal spatial misalignm...

GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care

Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervas...

DeepFreqMark: End-To-End Learnable Frequency-Domain Watermarking with Spherical Attack Simulation for Latent Diffusion Models

The proliferation of AI-generated images produced by Latent Diffusion Models (LDMs) has raised critical concerns regarding copyright infringement and ...

A Domain-Structured Ensemble Framework for Perioperative Outcome Prediction Using Electronic Health Record Data

Perioperative risk prediction models are often limited by narrow surgical populations, incomplete intraoperative data, poor calibration, and limited i...

Decoding Phenotypes: A Framework for Fusing Genomic Language Models and Neuroimaging

Neuroimaging and genetic testing are two important clinical references for nervous system diseases, offering complementary diagnostic information. How...

Multimodal Large Language Models vs. Medical Doctors in Degenerative Lumbar Spine Surgery: A Retrospective Decision Concordance Study of 147 Patients

Objective: To evaluate decision concordance between commercially available multimodal large language models (LLMs), resident doctors, and senior-surge...

Predicting Cognitive Function Using Transformer-Derived Speech Representations and Longitudinal Coherence Features

Dementia affects more than 55 million people worldwide, and its progressive decline is difficult to track using infrequent in-person assessments, whic...

A Paragraph is Worth a Thousand Captions: Rethinking Text Supervision for Vision-Language Retrieval

Contrastive vision-language models such as CLIP and BLIP are typically trained on short image captions, limiting their ability to retrieve images from...

Tau reduction counteracts transcriptomic and behavioral abnormalities in an Alzheimer amyloid model

Alzheimer's disease (AD) causes amyloid formation, neuritic dystrophy, gliosis, synapse loss, behavioral abnormalities, and weight loss. 5xFAD transge...

SPADE: An Input-Adaptive Sparse Attention Engine for Fast Video Diffusion Models Inference

Video diffusion transformers (vDiTs) generate high quality but pay quadratic self-attention cost, making inference prohibitive at video-token scales. ...