Latest AI and machine learning research in surveys for healthcare professionals.
INTRODUCTION: The aim of this study was to assess the overall performance of artificial intelligence (AI) chatbots in taking the American Board of Endodontics simulated Oral Board Examination. METHODS: Three oral board cases were constructed by 2 academic board-certified endodontists. Each case included a comprehensive patient profile consisting of medical history, dental history, and results of c...
BACKGROUND: Chronic diseases pose a heavy global burden, with challenges in utilizing unstructured data for continuous care. Natural language intelligence technology (NLIT) shows potential in addressing these issues. METHODS: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with its protocol register...
BACKGROUND AND OBJECTIVE: Manual data extraction is a major bottleneck in uro-oncology, limiting research and quality assurance. Although artificial i...
OBJECTIVES: Systematic evidence reviews (SERs) produced by the U.S. Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (...
Maternal undernutrition and micronutrient deficiencies remain pervasive, contributing to adverse pregnancy outcomes and long-term health risks for mot...
BACKGROUND: While behavioral interventions remain an evidence-based treatment for obesity, they often require long durations and frequent sessions. To...
BACKGROUND: In recent years, artificial intelligence (AI) systems have increasingly been used to assess emotional states in health care. AI offers a s...
BACKGROUND/AIMS: Patients have largely been excluded from discussions on the use of their health data in developing medical artificial intelligence (A...
BACKGROUND: Day of surgery cancellation (DOSC) for elective surgery occurs in 18% of elective surgeries worldwide with resultant impacts on patients a...
A growing number of data-driven clinical decision support (CDS) tools are incorporated into tele-critical care, but the clinician perceptions of their...
Split-learning-based Virtual Physically Unclonable Functions (VPUFs) in Internet of Things (IoT) networks remain vulnerable to eavesdropping and repla...
The Harvard-Emory ECG Database (HEEDB) is currently the largest open-access collection of 12-lead electrocardiogram (ECG) recordings, developed throug...
Large language models (LLMs) show potential in clinical reporting, yet current multimodal systems remain unreliable for interpreting panoramic radiogr...
Psychotic disorders are marked by heterogeneity in symptoms and treatment response, yet efforts to develop clinically useful predictive models through...
BACKGROUND: Machine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predic...
The folded-X pattern has been identified as a critical signature of confidence: as conditions become easier, confidence increases for correct trials b...
Based on neurocognitive models, the development and maintenance of post-traumatic stress disorder (PTSD) are correlated with cognitive biases, includi...
OBJECTIVE: To evaluate whether a rule-based artificial intelligence (AI) program can enhance interrater agreement in cardiotocography (CTG) interpreta...
Fault detection in IoT-enabled machinery involves identifying defects in the operation of industrial equipment to foil breakdowns that ensure reliabil...
INTRODUCTION: The automation of hazardous drug preparation in hospitals using robotic systems is an effective strategy to enhance safety, quality, and...