Latest AI and machine learning research in risk management for healthcare professionals.
Radiotherapy treatment planning is a resource-intensive process characterized by multiple manual steps and clinical hand-offs that contribute to treatment delays and inter-observer variability. The Radiation Planning Assistant (RPA) is a web-based platform designed to deliver automated contouring and planning approaches tailored to low-resource settings. This work expands the RPA to develop and cl...
Cancer therapeutic response patterns may be fundamentally influenced by embryonic germ layer origin. Emerging evidence suggests mesoderm-derived malignancies exhibit exceptional responsiveness to cellular immunotherapy, endoderm-derived epithelial cancers demonstrate marked sensitivity to protein signaling inhibitors, and ectoderm-derived tumors show heightened immunogenicity enabling breakthrough...
Fragile X Syndrome (FXS) is the most common inherited cause of intellectual disability and syndromic autism, but diagnosis remains challenging due to ...
Urogenital schistosomiasis caused by Schistosoma haematobium remains endemic in sub-Saharan Africa. Diagnosis traditionally relies on urine microscopy...
We evaluated whether oxytocin improves social-emotional reciprocity in children and adolescents with autism spectrum disorder (ASD) by conducting a se...
Medical jargon poses significant barriers to patient comprehension of healthcare information, potentially affecting treatment adherence and health out...
Psychotropic medications are commonly used for children with neurodevelopmental conditions, but their effectiveness varies, making treatment selection...
Precise glioma segmentation in MRI is essential for accurate diagnosis, optimal treatment planning, and advancing clinical research. However, most dee...
Gender-neutral patient education materials often overlook critical sex-based differences in cardiovascular disease (CVD). Large Language Models (LLMs)...
High-quality clinical documentation is essential for safe, effective care, yet producing it is time consuming and error prone. Large language models (...
Polygenic predictors can enhance screening for biomedical conditions, such as metabolism-related traits and diseases, but explain limited phenotypic v...
Primary care is facing multiple crises, including an increase in health misinformation. Digital health messaging by primary care providers has been sh...
Deviations from normative brain ageing trajectories are linked to a wide range of adverse health outcomes. A number of brain age prediction models hav...
Artificial intelligence (AI) models with medical images as input data are increasingly proposed to support clinical decisions in lung cancer screening...
Explainable AI (XAI) is essential in clinical machine learning, yet quantitative evaluation of explanation quality is rarely reported in a reproducibl...
Estimating an individual’s liability to a disease is a fundamental problem in genome research. By exploiting findings from genome-wide association stu...
Opioid analgesics are widely prescribed for pain, yet individuals vary markedly in their patterns of medical opioid use, influencing the risk of prolo...
INTRODUCTION: Delayed diagnosis of diabetic retinopathy (DR) remains a significant challenge, often leading to preventable blindness and visual impair...
BACKGROUND: Maternal and newborn mortality remains a critical public health challenge, particularly in resource-limited settings. Despite global effor...
Recently, dataset condensation has made significant progress in the image domain. Unlike images, videos possess an additional temporal dimension, wh...