A5088789487- Radiology practices and education
- COVID-19 diagnosis using AI
- Lung Cancer Diagnosis and Treatment
- Radiomics and Machine Learning in Medical Imaging
- Artificial Intelligence in Healthcare and Education
- Liver Disease and Transplantation
- Health Policy Implementation Science
- Liver Disease Diagnosis and Treatment
- Clinical Reasoning and Diagnostic Skills
- Urinary Bladder and Prostate Research
- Airway Management and Intubation Techniques
- Urologic and reproductive health conditions
- Urological Disorders and Treatments
- Liver Diseases and Immunity
- Ultrasound in Clinical Applications
- Helminth infection and control
- Health Sciences Research and Education
- Medical Imaging and Analysis
Oxford University Hospitals NHS Trust
2024-2025
Artificial intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. Most research to date has focused on the performance AI-assisted algorithms in comparison with that radiologists rather than evaluating algorithms' impact clinicians who often undertake initial routine practice. This study assessed diagnostic frontline acute care for detection pneumothoraces (PTX).
Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances computer vision have led artificial intelligence (AI)-enhanced model developments, which can support detection fractures. Previous research has shown these models promising effects on performance, but their impact accuracy National Health Service (NHS) setting not yet been fully evaluated.
Introduction A chest X-ray (CXR) is the most common imaging investigation performed worldwide. Advances in machine learning and computer vision technologies have led to development of several artificial intelligence (AI) tools detect abnormalities on CXRs, which may expand diagnostic support a wider field health professionals. There paucity evidence impact AI algorithms assisting healthcare professionals (other than radiologists) who regularly review CXR images their daily practice. Aims To...
Background: Artificial Intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. To date research has focussed on AI’s performance with radiologists rather than, non-radiologist clinicians who often undertake initial interpretation. This study assessed the impact AI-assisted diagnostic frontline acute care for detection pneumothoraces.Methods: A multicentre blinded fully-crossed multi-case multi-reader was conducted between October 2021 to January...