Staffing & Scheduling

Pathways to democratized healthcare: Envisioning human-centered AI-as-a-service for customized diagnosis and rehabilitation.

The ongoing digital revolution in the healthcare sector, emphasized by bodies like the US Food and Drug Administration (FDA), is paving the way for a shift towards person-centric healthcare models. These models consider individual needs, turning patients from passive recipients to active participants. A key factor in this shift is Artificial Intelligence (AI), which has the capacity to revolutioni...

Can biased search results change people's opinions about anything at all? a close replication of the Search Engine Manipulation Effect (SEME).

In previous experiments we have conducted on the Search Engine Manipulation Effect (SEME), we have focused on the ability of biased search results to shift voting preferences. In three new experiments with a total of 1,137 US residents (mean age = 33.2), we sought to determine whether biased search rankings could shift people's opinions on topics that do not involve candidates or elections. Each o...

A paradigm shift?-On the ethics of medical large language models.

After a wave of breakthroughs in image-based medical diagnostics and risk prediction models, machine learning (ML) has turned into a normal science. H...

Correlating Age and Hematoma Volume with Extent of Midline Shift in Acute Subdural Hematoma Patients: Validation of an Artificial Intelligence Tool for Volumetric Analysis.

OBJECTIVE: Decision for intervention in acute subdural hematoma patients is based on a combination of clinical and radiographic factors. Age has been ...

[Development of an artificial intelligence system to improve cancer clinical trial eligibility screening].

INTRODUCTION: The recruitment step of all clinical trials is time consuming, harsh and generate extra costs. Artificial intelligence tools could impro...

Artificial intelligence tools for optimising recruitment and retention in clinical trials: a scoping review protocol.

INTRODUCTION: In recent years, the influence of artificial intelligence technology on clinical trials has been steadily increasing. It has brought abo...

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction.

The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for...

A Combination Model of Shifting Joint Angle Changes With 3D-Deep Convolutional Neural Network to Recognize Human Activity.

Research in the field of human activity recognition is very interesting due to its potential for various applications such as in the field of medical ...

Enhancing site selection strategies in clinical trial recruitment using real-world data modeling.

Slow patient enrollment or failing to enroll the required number of patients is a disruptor of clinical trial timelines. To meet the planned trial rec...

Simultaneous high-definition transcranial direct current stimulation and robot-assisted gait training in stroke patients.

This study investigates whether simultaneous high-definition transcranial direct current stimulation (HD-tDCS) enhances the effects of robot-assisted ...

Anomaly detection of retention loss in fixed partial dentures using resonance frequency analysis and machine learning: An in vitro study.

PURPOSE: This study aimed to determine the usefulness of machine learning techniques, specifically supervised and unsupervised learning, for assessing...

Deep learning for head and neck semi-supervised semantic segmentation.

. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the preci...

Nanoinformatics based insights into the interaction of blood plasma proteins with carbon based nanomaterials: Implications for biomedical applications.

In the past three decades, interest in using carbon-based nanomaterials (CBNs) in biomedical application has witnessed remarkable growth. Despite the ...

Towards a better negative sampling strategy for dynamic graphs.

As dynamic graphs have become indispensable in numerous fields due to their capacity to represent evolving relationships over time, there has been a c...

Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs.

Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Allevia...

Bias reduction using combined stain normalization and augmentation for AI-based classification of histological images.

Artificial intelligence (AI)-assisted diagnosis is an ongoing revolution in pathology. However, a frequent drawback of AI models is their propension t...

DBNet-SI: Dual branch network of shift window attention and inception structure for skin lesion segmentation.

The U-shaped convolutional neural network (CNN) has attained remarkable achievements in the segmentation of skin lesion. However, given the inherent l...

Point-Of-Care low-field MRI in acute Stroke (POCS): protocol for a multicentric prospective open-label study evaluating diagnostic accuracy.

INTRODUCTION: Fast and accurate diagnosis of acute stroke is crucial to timely initiate reperfusion therapies. Conventional high-field (HF) MRI yields...

Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus.

Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between...

A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies.

Physiotherapy is a critical area of healthcare that involves the assessment and treatment of physical disabilities and injuries. The use of Artificial...