Latest AI and machine learning research in product alert for healthcare professionals.
BACKGROUND: Machine learning (ML) applications in clinical medicine are vulnerable to data leakage, particularly temporal leakage from post-diagnostic features and patient-level leakage from improper partitioning, compromising electrocardiogram (ECG) abnormality detection systems. This study addresses these vulnerabilities through patient-level data splitting and systematic evaluation across multi...
BACKGROUND: Large language models (LLMs) are increasingly explored for drug information support, yet their reliability and clinical applicability remain uncertain. This study evaluated multiple LLMs in responding to real-world drug information questions retrieved from a university hospital in Thailand, focusing on clarity in Thai, concordance with pharmacist responses, relevance, context awareness...
BACKGROUND: Accurate delineation of the prostate and surrounding organs-at-risk (OARs) is essential for HDR prostate brachytherapy. Manual contouring ...
Cryo-electron microscopy (cryo-EM) micrographs are frequently contaminated by carbon edges, ice crystals, ethane bubbles and other high-contrast artif...
BACKGROUND: Prior authorization (PA) is intended to support appropriate use and spending of services and medications, yet 1 in 6 insured adults report...
This study evaluated district-wide implementation of a digital wound model of care combining an artificial intelligence-enabled application with a vir...
PURPOSE: Traditional drug-induced liver injury (DILI) surveillance relying on static laboratory thresholds frequently misses early kinetic evolution. ...
BACKGROUND: Traditional rehabilitation medicine, primarily dependent on qualitative clinical assessment and static therapeutic protocols, faces signif...
BACKGROUND: Post-traumatic stress disorder (PTSD) symptoms can fluctuate substantially over short periods, yet routine screening typically relies on i...
BACKGROUND: Call abandonment is a critical barrier to patient access in health care call centers; yet, predictive modeling efforts are limited by stri...
OBJECTIVES: Artificial intelligence (AI) is increasingly used in esthetic dentistry; however, its accuracy in predicting post-prosthetic facial outcom...
BACKGROUND: Timely vasopressor initiation is critical in fluid-refractory pediatric septic shock, yet clinicians lack objective tools to identify chil...
BACKGROUND: Falls are among the most common adverse events in hospitalized patients, with about 30% leading to injury. We developed a machine learning...
OBJECTIVE: Interpretable brain-computer interface classifiers that generalize across subjects without calibration remain an open challenge. We evaluat...
BACKGROUND: Stigmatizing language (SL) in electronic health records (EHRs) can influence clinical decision-making, propagate bias across care encounte...
BACKGROUND: Generative AI lowers the technical barrier to clinician-led software development, but functional success and usability do not establish cl...
Accurate symptom-to-disease classification and clinically-grounded treatment recommendations remain challenging, particularly in heterogeneous patient...
Surgical resection, and its associated bowel preparation, remain the primary treatment for colorectal cancer (CRC), yet the associated effects on post...
Artificial intelligence (AI) is increasingly being explored to support pharmacovigilance activities including processes involving individual case safe...
BACKGROUND: Cardiovascular events are a leading cause of mortality after liver transplantation (LT), and existing risk scores incompletely capture myo...