Surveys

Artificial intelligence-enabled digital reproductive health education: corpus-based strategies for strengthening Spanish health communication capacity.

This study examines how corpus-grounded artificial intelligence (AI) can strengthen Spanish reproductive health communication capacity within China's digital health ecosystem. A mixed-methods design was employed, combining corpus linguistics, AI-assisted message generation, expert-informed evaluation, and quantitative user assessment. A domain-specific Spanish reproductive health corpus was constr...

Diet, Diarrhoea and Maternal Factors Associated With Wasting in Ghanaian Children Aged 6-23 Months: A Machine Learning Predictive Approach Using the 2022 Demographic and Health Survey.

OBJECTIVES: This study estimates the prevalence of wasting and examines associated factors among children aged 6-23 months in Ghana from a machine-learning perspective, with a particular focus on predicting new cases to inform preventive efforts. METHODS: This study is a secondary analysis of the 2022 Ghana Demographic and Health Survey, which includes 1506 children. Wasting status was assessed us...

Evaluating cognitive biases in AI-assisted mammography interpretation: a simulation reader study of explainable AI across radiologist experience levels.

OBJECTIVES: To evaluate the impact of automation and anchoring bias in artificial intelligence (AI)-assisted mammography interpretation and to assess ...

Local Semantics Refinement of Adaptive Representations for Robust Noisy Label Learning.

The success of deep learning models heavily depends on high-quality labeled data, yet noisy labels are inevitable in large-scale datasets. Existing me...

A hybrid AI vision model for single-camera tracking of rehabilitation exercises: a proof of concept in clinical settings.

Accurate measurement of range of motion (ROM) during physical rehabilitation is traditionally achieved using goniometers or multi-camera, marker-based...

Detecting dataset bias in medical AI using a generalized and modality agnostic auditing approach.

Despite many success stories along the path of Artificial Intelligence's (AI) rise in healthcare, there are comparably many reports of significant sho...

The impact of an artificial intelligence (AI)-assisted education program on mental health, social support and quality of life in older individuals with head and neck cancer: study protocol of a randomized controlled trial.

BACKGROUND: Head and neck cancer (HNC) is a common malignant tumor, and its treatment often leads to functional impairments in speech, swallowing, and...

A survey of deep learning techniques in detecting neurological disorders using MRI.

Magnetic resonance imaging (MRI) is widely regarded as the most reliable non-invasive imaging modality for detecting neurological disorders. However, ...

Comparison of linear and angular cephalometric measurements in CBCT using semi-automated software and artificial intelligence-generated cephalograms.

BACKGROUND: This study aimed to assess the reliability of cephalometric measurements using CBCTs with two semi-automated software programs (InVivoDent...

FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation.

Here we introduce FLOWR, a structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates continuous and...

Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment.

Artificial intelligence (AI) can transform cancer immunotherapy by enabling more accurate prediction of treatment responses, the discovery of specific...

Evaluation of the accuracy, quality, and readability of large language models on local anesthesia and general anesthesia-sedation in dentistry.

PURPOSE: This study aims to compare the responses provided by commonly used artificial intelligence-based chatbots such as ChatGPT-3.5, ChatGPT-4o, Ge...

Machine learning for postoperative complication prediction and early recurrence risk assessment across cancer types: a systematic review and meta-analysis.

BACKGROUND: Although machine learning is often used in medical diagnosis, its effectiveness in cancer diagnosis remains uncertain. OBJECTIVE: To explo...

Perception of artificial intelligence and machine learning applications in the Nigerian healthcare sector: A cross-sectional study.

Artificial intelligence (AI) has significant benefits across various facets of healthcare delivery, notably in personalised pharmacotherapy and the de...

An integrated wireless deep-UV sensing system for intelligent early fire detection.

Uncontrolled fires, from wildlands to industrial facilities, have become a pressing global threat, causing widespread ecological damage, loss of life,...

AI-assisted health management and older adults' autonomy: an empirical ethics study of privacy, algorithmic reliance, and relational dynamics.

BACKGROUND: AI-assisted health management is increasingly integrated into healthcare practices for older adults, offering new possibilities for contin...

Development and validation of the Dental Artificial Intelligence Readiness and Acceptance Instrument (DAI-RAI) for dental professionals.

BACKGROUND: Artificial intelligence (AI), including applications such as radiographic image analysis, caries detection, and treatment planning, is inc...

Quality and performance of machine learning versus logistic regression for predicting IVIG resistance in Kawasaki disease: a PROBAST+AI systematic comparison.

BACKGROUND: This study aimed to systematically compare the predictive performance and methodological quality of logistic regression (LR) and machine l...

Predicting low birth weight in Bangladesh using interpretable machine learning models.

BACKGROUND: Low birth weight (LBW) remains a leading cause of neonatal mortality and long-term morbidity in low- and middle-income countries. This stu...

Improving Clinical Efficiency Using Artificial Intelligence Scribe for Documentation (ICE-AID Study): A Retrospective-Prospective Cohort Study at a Quaternary Children's Hospital and Health Service.

AIM: To evaluate the impact of an artificial intelligence medical scribe (AIMS) on clinical documentation efficiency, document quality, clinician-pati...