Latest AI and machine learning research in adhd/add for healthcare professionals.
Image steganalysis, the detection of hidden information embedded in digital images, is a core component of modern cybersecurity and digital forensics. Recent residual Transformer architectures, such as the Pixel-Difference-Convolution and Enhanced-Transformer-Network (PENet) [1], achieve strong detection accuracy, but their computational and memory demands hinder deployment in resource-constrained...
Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cost, wearable-friendly cuffless BP estimation. However, even with recent progress, many PPG-based models are trained with BP regression alone and may rely on amplitude-dominated shortcuts. In addition, demographic covariates that systematically modulat...
End-of-rotation handoffs are critical for patient safety but add to documentation burden for hospitalists. Generative artificial intelligence (AI) may...
Multi-contrast brain MRI provide complementary soft-tissue characteristics that aid in the screening and diagnosis of diseases. However, limited scann...
Large language models embedded in autonomous agents process trusted instructions and untrusted data in one context window, leaving them open to direct...
The generation of high-fidelity synthetic Electronic Health Records (EHR) is crucial for advancing medical research while preserving patient privacy. ...
Diffusion-based vision-language-action (VLA) models often inherit the image-generation view: actions are generated by iterative denoising. We argue th...
Developing unified video generation and editing models capable of interpreting interleaved multimodal inputs is a promising yet challenging frontier f...
Phylogenetic inference is a common task in molecular and evolutionary biology and has conventionally required a multiple sequence alignment (MSA), a s...
Importance: Prenatal exposure to gestational diabetes mellitus (GDM) has been associated with adverse metabolic, neurodevelopmental, and psychiatric o...
Concept unlearning aims to erase a target concept from a pretrained text-to-image diffusion model without retraining. Closed-form methods are attracti...
Recommender systems generally optimises user engagement, but this approach is dangerous in mental health contexts. When vulnerable users show signs of...
Background Background breast features are frequently noted in pathology reports alongside invasive breast cancer but rarely factor into prognosis or t...
Autonomous rendezvous and proximity operations around uncooperative, unknown spacecraft are critical for active debris removal and on-orbit servicing ...
Agentic tools - software environments where a large language model plans, calls external tools, executes code, and iterates with minimal human interve...
Recent advances in generative models have empowered impressive layered image generation, yet their success is largely confined to graphic design domai...
By processing electronic health records (EHRs) as natural language sequences, large language models (LLMs) have shown potential in clinical prediction...
Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independen...
Phylodynamics bridges the gap between epidemiology and pathogen genetic data by estimating epidemiological parameters from time-scaled pathogen phylog...
Discrete image tokenizers are commonly trained in two stages: first for reconstruction, and then with a prior model fitted to the frozen token sequenc...