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Effect of cooking and food serving robot design images and information on consumer liking, willingness to try food, and emotional responses.

Food research international (Ottawa, Ont.)
The utilization of robots in the food industry, including restaurants and cafés, has increased in recent years. This study investigated participants' responses to robots in the serving and cooking domains, which require varying degrees of consumer in...

Nomograms versus artificial intelligence platforms: which one can better predict sentinel node positivity in melanoma patients?

Melanoma research
Nomograms are commonly used in oncology to assist clinicians in individualized decision-making processes, such as considering sentinel node biopsy (SNB) for melanoma patients. Concurrently, artificial intelligence (AI) is increasingly being utilized ...

From Guidelines to Intelligence: How AI Refines Thyroid Nodule Biopsy Decisions.

Ultrasound in medicine & biology
OBJECTIVE: To evaluate the value of combining American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) with the Demetics ultrasound diagnostic system in reducing the rate of fine-needle aspiration (FNA) biopsies for thy...

Comparison of quality, empathy and readability of physician responses versus chatbot responses to common cerebrovascular neurosurgical questions on a social media platform.

Clinical neurology and neurosurgery
BACKGROUND: Social media platforms are utilized by patients prior to scheduling formal consultations and also serve as a means of pursuing second opinions. Cerebrovascular pathologies require regular surveillance and specialized care. In recent years...

Discriminating Clear Cell From Non-Clear Cell Renal Cell Carcinoma: A Machine Learning Approach Using Contrast-enhanced Ultrasound Radiomics.

Ultrasound in medicine & biology
OBJECTIVE: The aim of this investigation is to assess the clinical usefulness of a machine learning model using contrast-enhanced ultrasound (CEUS) radiomics in discriminating clear cell renal cell carcinoma (ccRCC) from non-ccRCC.

CLABpredICU---AI-driven risk prediction for CLABSI in intensive care units based on clinical and biochemical parameters.

American journal of infection control
BACKGROUND: Central line--associated bloodstream infections (CLABSI) are major causes of morbidity and mortality in intensive care units. This study aimed to develop an artificial intelligence-driven predictive model for CLABSI within 2 calendar days...

Applying exposure-response analysis to enhance Mycophenolate Mofetil dosing precision in pediatric patients with immune-mediated renal diseases by machine learning models.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
BACKGROUND: Mycophenolate mofetil (MMF), a cornerstone immunosuppressant for lupus nephritis, is increasingly used off-label in pediatric immune-mediated renal diseases. The aims of this study were to develop and validate pharmacokinetic models for m...

Machine Learning Models of Voxel-Level [F] Fluorodeoxyglucose Positron Emission Tomography Data Excel at Predicting Progressive Supranuclear Palsy Pathology.

Annals of neurology
OBJECTIVE: To determine whether a machine learning model of voxel level [f]fluorodeoxyglucose positron emission tomography (PET) data could predict progressive supranuclear palsy (PSP) pathology, as well as outperform currently available biomarkers.

Machine-learning modeL based on computed tomography body composition analysis for the estimation of resting energy expenditure: A pilot study.

Clinical nutrition ESPEN
BACKGROUND & AIMS: The assessment of resting energy expenditure (REE) is a challenging task with the current existing methods. The reference method, indirect calorimetry (IC), is not widely available, and other surrogates, such as equations and bioim...

Integrating Large language models into radiology workflow: Impact of generating personalized report templates from summary.

European journal of radiology
OBJECTIVE: To evaluate feasibility of large language models (LLMs) to convert radiologist-generated report summaries into personalized report templates, and assess its impact on scan reporting time and quality.