Latest AI and machine learning research in health policy for healthcare professionals.
Objective To characterize the clinical and administrative concerns communicated through secure ophthalmology messaging and to assess differences in message content across patient sociodemographic groups. Design Cross-sectional study of de-identified, patient-initiated secure messages sent between June 2014 and July 2024. Participants Patients with ophthalmic conditions who initiated secure electro...
Generative text-to-image models are advancing at an unprecedented pace, continuously shifting the perceptual quality ceiling and rendering previously collected labels unreliable for newer generations. To address this, we present ELIQ, a Label-free Framework for Quality Assessment of Evolving AI-generated Images. Specifically, ELIQ focuses on visual quality and prompt-image alignment, automatically...
Telecommunications networks generate extensive performance and environmental telemetry, yet most LTE and 5G-NR deployments still rely on static, manua...
Deploying ADAS and ADS across countries remains challenging due to differences in legislation, traffic infrastructure, and visual conventions, which i...
Background: Generating synthetic data using artificial intelligence, such as large language models (LLMs), is a useful strategy in public health becau...
Image Quality Assessment (IQA) predicts perceptual quality scores consistent with human judgments. Recent RL-based IQA methods built on MLLMs focus on...
Limited access to medical infrastructure forces elderly and vulnerable patients to rely on home-based care, often leading to neglect and poor adherenc...
Background: Human immunodeficiency virus (HIV) disproportionately affects marginalized communities in the United States, with Black Americans comprisi...
Recent advances in text-to-image (T2I) diffusion models have enabled increasingly realistic synthesis of vehicle damage, raising concerns about their ...
Many reinforcement learning (RL) problems admit multiple terminal solutions of comparable quality, where the goal is not to identify a single optimum ...
Large Language Models (LLMs) can be fine-tuned on domain-specific data to enhance their performance in specialized fields. However, such data often co...
Accessing high-quality, open-access dermatopathology image datasets for learning and cross-referencing is a common challenge for clinicians and dermat...
Nutritional interventions are important for managing chronic health conditions, but current computational methods provide limited support for personal...
Rubrics are essential for evaluating open-ended LLM responses, especially in safety-critical domains such as healthcare. However, creating high-qualit...
Healthcare institutions have access to valuable patient data that could be of great help in the development of improved diagnostic models, but privacy...
Healthcare visitation patterns are influenced by a complex interplay of hospital attributes, population socioeconomics, and spatial factors. However, ...
Importance: Emerging evidence suggests healthcare AI systems may exhibit deceptive alignment (appearing safe during validation while optimizing for mi...
Artificial Intelligence-Generated Content (AIGC) has made significant strides, with high-resolution text-to-image (T2I) generation becoming increasing...
Large-scale medical segmentation datasets often combine manual and pseudo-labels of uneven quality, which can compromise training and evaluation. Low-...
Increased access to reliable health information is essential for non-English-speaking populations, yet resources in Bangla for disease prediction rema...