An experimental machine learning study investigating the decision-making process of students and qualified radiographers when interpreting radiographic images

C Rainey, AT Villikudathil, J McConnell… - PLOS Digital …, 2023 - journals.plos.org
AI is becoming more prevalent in healthcare and is predicted to be further integrated into
workflows to ease the pressure on an already stretched service. The National Health Service …

Efficiency, accuracy, and health professional's perspectives regarding artificial intelligence in radiology practice: A scoping review

C He, W Liu, J Xu, Y Huang, Z Dong, Y Wu… - …, 2024 - Wiley Online Library
In this scoping review, we evaluated the performance of artificial intelligence (AI) in clinical
radiology practice and examined health professionals' perspectives regarding AI use in …

[HTML][HTML] Artificial intelligence for radiotherapy auto-contouring: Current use, perceptions of and barriers to implementation

S Hindocha, K Zucker, R Jena, K Banfill, K Mackay… - Clinical Oncology, 2023 - Elsevier
Aims Artificial intelligence has the potential to transform the radiotherapy workflow, resulting
in improved quality, safety, accuracy and timeliness of radiotherapy delivery. Several …

[HTML][HTML] AI implementation in the UK landscape: Knowledge of AI governance, perceived challenges and opportunities, and ways forward for radiographers

N Stogiannos, T O'Regan, E Scurr, L Litosseliti… - Radiography, 2024 - Elsevier
Introduction Despite the rapid increase of AI-enabled applications deployed in clinical
practice, many challenges exist around AI implementation, including the clarity of …

[HTML][HTML] Nordic radiographers' and students' perspectives on artificial intelligence–A cross-sectional online survey

MRV Pedersen, MW Kusk, S Lysdahlgaard… - Radiography, 2024 - Elsevier
Introduction The integration of artificial intelligence (AI) into the domain of radiography holds
substantial potential in various aspects including workflow efficiency, image processing …

Exploring the utility of cardiovascular magnetic resonance radiomic feature extraction for evaluation of cardiac sarcoidosis

NA Mushari, G Soultanidis, L Duff, MG Trivieri… - Diagnostics, 2023 - mdpi.com
Background: The aim of this study is to explore the utility of cardiac magnetic resonance
(CMR) imaging of radiomic features to distinguish active and inactive cardiac sarcoidosis …

[HTML][HTML] The American Society of Radiologic Technologists (ASRT) AI educator survey: A cross-sectional study to explore knowledge, experience, and use of AI within …

N Stogiannos, M Jennings, C St George… - Journal of Medical …, 2024 - Elsevier
Abstract Introduction Artificial Intelligence (AI) is revolutionizing medical imaging and
radiation therapy. AI-powered applications are being deployed to aid Medical Radiation …

[HTML][HTML] A Nordic survey on artificial intelligence in the radiography profession–Is the profession ready for a culture change?

MRV Pedersen, MW Kusk, S Lysdahlgaard… - Radiography, 2024 - Elsevier
Introduction The impact of artificial intelligence (AI) on the radiography profession remains
uncertain. Although AI has been increasingly used in clinical radiography, the perspectives …

Current Radiology workforce perspective on the integration of artificial intelligence in clinical practice: A systematic review

S Arkoh, TN Akudjedu, C Amedu, WK Antwi… - Journal of Medical …, 2025 - Elsevier
Abstract Introduction Artificial Intelligence (AI) represents the application of computer
systems to tasks traditionally performed by humans. The medical imaging profession has …

An assessment of PET and CMR radiomic features for detection of cardiac sarcoidosis

NA Mushari, G Soultanidis, L Duff… - Frontiers in Nuclear …, 2024 - frontiersin.org
Background Visual interpretation of PET and CMR may fail to identify cardiac sarcoidosis
(CS) with high specificity. This study aimed to evaluate the role of [18F] FDG PET and late …