AIMC Topic: Australia

Clear Filters Showing 201 to 210 of 249 articles

Temporal validation of machine learning models for pre-eclampsia prediction using routinely collected maternal characteristics: A validation study.

Computers in biology and medicine
BACKGROUND: Pre-eclampsia (PE) contributes to more than one-fourth of all maternal deaths and half a million newborn deaths worldwide every year. Early screening and interventions can reduce PE incidence and related complications. We aim to 1) tempor...

Artificial intelligence (AI) use for personal protective equipment training, remediation, and education in health care.

American journal of infection control
BACKGROUND: Personal protective equipment (PPE) is a first-line transmission-based precaution for reducing the spread of nosocomial infections between health care workers (HCWs), patients, and staff. The COVID-19 pandemic highlighted a problematic sk...

Evaluation of artificial intelligence (AI) chatbots for providing sexual health information: a consensus study using real-world clinical queries.

BMC public health
INTRODUCTION: Artificial Intelligence (AI) chatbots could potentially provide information on sensitive topics, including sexual health, to the public. However, their performance compared to nurses and across different AI chatbots, particularly in the...

Dual-stream algorithms for dementia detection: Harnessing structured and unstructured electronic health record data, a novel approach to prevalence estimation.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Identifying individuals with dementia is crucial for prevalence estimation and service planning, but reliable, scalable methods are lacking. We developed novel set algorithms using both structured and unstructured electronic health reco...

Implementing artificial intelligence in breast cancer screening: Women's preferences.

Cancer
BACKGROUND: Artificial intelligence (AI) could improve accuracy and efficiency of breast cancer screening. However, many women distrust AI in health care, potentially jeopardizing breast cancer screening participation rates. The aim was to quantify c...

Understanding Public Judgements on Artificial Intelligence in Healthcare: Dialogue Group Findings From Australia.

Health expectations : an international journal of public participation in health care and health policy
INTRODUCTION: There is a rapidly increasing number of applications of healthcare artificial intelligence (HCAI). Alongside this, a new field of research is investigating public support for HCAI. We conducted a study to identify the conditions on Aust...

The use of robotic upper limb therapy in routine clinical practice for stroke survivors: Insights from Australian therapists.

Australian occupational therapy journal
INTRODUCTION: There is a limited understanding of therapist acceptance and use of robot-assisted upper limb therapy (RT-ULT) in routine practice. The aim of this study was to explore the factors that influence Australian therapist acceptance and use ...

Predicting amyloid beta accumulation in cognitively unimpaired older adults: Cognitive assessments provide no additional utility beyond demographic and genetic factors.

Alzheimer's & dementia : the journal of the Alzheimer's Association
BACKGROUND: Integrating non-invasive measures to estimate abnormal amyloid beta accumulation (Aβ+) is key to developing a screening tool for preclinical Alzheimer's disease (AD). The predictive capability of standard neuropsychological tests in estim...

How should artificial intelligence be used in breast screening? Women's reasoning about workflow options.

PloS one
Studies show that breast screening participants are open to artificial intelligence (AI) in breast screening, but hold concerns about AI performance, governance, equitable access, and dependence on technology. Little is known of consumers' views on h...

Radiomic analysis of cohort-specific diagnostic errors in reading dense mammograms using artificial intelligence.

The British journal of radiology
OBJECTIVES: This study aims to investigate radiologists' interpretation errors when reading dense screening mammograms using a radiomics-based artificial intelligence approach.