Latest AI and machine learning research in information technology for healthcare professionals.
Neural data collected using brain-computer interfaces, neural implants, and emotion detection systems is analyzed by AI classifiers and agentic architectures to serve purposes such as authentication, access control, and behavioral inference, however, there exists no comprehensive, binding cybersecurity or data protection regime to regulate such neural data. The regulations that currently exist i.e...
Acoustic analysis is a fundamental step in the multidimensional assessment of dysphonia. However, conventional perturbation measures require nearly periodic signals for valid computation. Visual signal typing classifies voice signals into discrete types based on a narrowband spectrogram and serves as a critical gatekeeper for selecting appropriate analytical methods for a valid assessment. Two mai...
INTRODUCTION: This study aimed to examine the perceptions of Greek radiographers and radiologists on integrating artificial intelligence (AI) in medic...
BACKGROUND: Stigmatizing language (SL) in electronic health records (EHRs) can influence clinical decision-making, propagate bias across care encounte...
IMPORTANCE: Surgical site infections (SSIs) are common postoperative complications, often detected after patients leave the hospital, especially in ti...
INTRODUCTION: Vancomycin is widely used for severe Gram‑positive infections in children, but vancomycin‑induced nephrotoxicity (VIN) limits its safe a...
BACKGROUND: Digital transformation through electronic health records (EHRs), telehealth, mobile health, and emerging AI has reshaped nursing work. Bey...
BACKGROUND AND PURPOSE: Small intracerebral hemorrhage (ICH), defined as baseline NCCT hematoma volume (HV) <30 mL, is often considered lower risk for...
The terminology "Biobank" is used for the organized collection of biological materials consisting of tissue samples, blood, serum, body fluids, and DN...
Generative AI, particularly large language models (LLMs), is reshaping clinical workflows in dermatology. However, cloud-based commercial models pose ...
The escalating prevalence of multidrug-resistant bacterial infections presents a grave global health challenge, highlighting the limitations of tradit...
Generative artificial intelligence (AI) is dramatically changing the division of labor in digital health innovation. Until recently, a frontline healt...
Immunotherapies such as chimeric antigen receptor T cell (CAR-T) therapy and immune checkpoint inhibitors have improved cancer survival but are associ...
Effects of stroke therapies area highly time dependent but onset-to-treatment times for recanalizing treatment are mostly beyond optimal time windows....
PURPOSE: The adoption of electronic medical records (EMRs) has expanded research opportunities. International Classification of Diseases (ICD) codes a...
BACKGROUND: Semantic interoperability, the ability of disparate health information systems to exchange and consistently interpret clinical data, is a ...
Diabetic retinopathy (DR) remains a leading cause of blindness globally, driving the rapid development of automated diagnostic systems leveraging deep...
Endoscopic diagnostics and treatment of early esophageal neoplasia have achieved considerable progress in recent years. For Barrett's esophagus-associ...
Secondary use is now the ordinary condition of data science health research rather than an exception to it. Electronic health records collected for cl...
Aptamers are widely used in biosensing and targeted therapeutics, yet reported data remain fragmented across unstructured text, tables, and figures. E...