Latest AI and machine learning research in surveys for healthcare professionals.
Perceived trustworthiness of research may be influenced by factors beyond the risk of bias, including study-related characteristics, research context, and external circumstances. Identifying these factors is essential for gauging the credibility of non-randomized studies of interventions (NRSIs) as they are interpreted and used in systematic reviews, and for improving their design to ensure that t...
Prostate cancer diagnosis and treatment planning depend on accurate histopathological assessment of needle biopsies, particularly through the Gleason scoring system. The inherently subjective nature of the grading creates variability between pathologists, potentially resulting in suboptimal patient management decisions. These reproducibility challenges extend beyond Gleason scoring to encompass ot...
Hypertension is a major modifiable risk factor for cardiovascular diseases. Early identification of high-risk individuals using predictive models can ...
Two sound level maps currently exist for the contiguous United States. One was developed by the National Park Service (NPS) using machine learning met...
To develop privacy-enhancing statistical methods for estimation of binary disease risk model association parameters across multiple electronic health ...
This study investigates how historical disparities in healthcare access influence machine learning (ML) predictions of healthcare utilization among ol...
Parent/patient-reported datasets provide ready access to phenotypic data for monogenic neurodevelopmental disorders yet their concordance with clinica...
Type 2 diabetes mellitus (T2DM) affects almost half a billion people, and the projected cost is $2.25 trillion by 2030; early detection strategies are...
Large language models (LLMs) have demonstrated rapid advancements in natural language understanding and generation, prompting their integration into b...
Spinal cord injury (SCI) remains a devastating neurological condition with high global incidence and minimal curative options. The pathobiology is mul...
Large language models (LLMs) show promise on healthcare tasks, yet most evaluations emphasize multiple-choice accuracy rather than open-ended reasonin...
Electronic Health Records (EHRs) store vast amounts of clinical information that are difficult for healthcare providers to summarize and synthesize re...
Epilepsy affects approximately 50 million people worldwide, and seizures remain difficult to predict in onset, severity, and duration. Real-time seizu...
Multimorbidity, the coexistence of multiple chronic conditions, is a growing public health challenge, particularly in low- and middle-income countries...
Accelerometers are used to measure sedentary time (SED) and physical activity (PA) in toddlers, but they may struggle to wear them for extended period...
The National Early Warning Score (NEWS) is a nationally recommended, clinically implemented system, used to prevent patient deterioration. While numer...
Primary care artificial intelligence adoption among United States (US) physicians accelerated from 38% to 66% within one year. Implementation strategi...
Cancer therapeutic response patterns may be fundamentally influenced by embryonic germ layer origin. Emerging evidence suggests mesoderm-derived malig...
The aim of the present study is to systematically investigate the phenomenon of Conformity Bias in contemporary LLMs, specifically evaluating how repe...
Large language models (LLMs) are increasingly used in healthcare, but standardized benchmarks fail to capture their validity and safety in real-world ...