| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
Accuracy of Eyes of AI Artificial Intelligence-Driven Platform for Lateral Cephalometric Analysis
|
Le, Sen
|
|
|
3 |
S1 |
p.
|
article
|
| 2 |
Accuracy of indigenous AI tool in detection of ischaemic neurological stroke
|
Jain, Neha
|
|
|
3 |
S1 |
p.
|
article
|
| 3 |
A comparative analysis of artificial intelligence and human performance in detecting wrist fractures on X-rays using MRI as the gold standard
|
Basu, Subhasis
|
|
|
3 |
S1 |
p.
|
article
|
| 4 |
A comparative evaluation of bone age estimation models
|
Brzozowski, Daniel
|
|
|
3 |
S1 |
p.
|
article
|
| 5 |
AI as a Medical Device for autonomous reporting of chest radiographs: a large-centre retrospective validation
|
Kale, Aditya
|
|
|
3 |
S1 |
p.
|
article
|
| 6 |
AI-assisted spine MRI reporting: a correlation study with radiologist-derived conclusions
|
Ala’Aldeen, Avesta
|
|
|
3 |
S1 |
p.
|
article
|
| 7 |
AI-CXR triage expedites lung cancer investigation in the NHS
|
Storey, Mathew
|
|
|
3 |
S1 |
p.
|
article
|
| 8 |
AI-driven 3D reconstruction and automated detection of spinal pathologies for enhanced radiological diagnostics
|
Medepalli, Yadidya
|
|
|
3 |
S1 |
p.
|
article
|
| 9 |
AI-Driven Exam Results Dashboards: Transforming Educational Decision-Making in Pakistan
|
Zai, Sajid Yousuf
|
|
|
3 |
S1 |
p.
|
article
|
| 10 |
AI-powered adaptive learning platform: driving innovation in healthcare education and research
|
Pal, Sanchita
|
|
|
3 |
S1 |
p.
|
article
|
| 11 |
AI-powered triage for acute MSK MRI requests: improving workflow efficiency and clinical decision-making
|
Ali, Walid
|
|
|
3 |
S1 |
p.
|
article
|
| 12 |
AI predicts normal chest radiographs in real-world NHS practice
|
Storey, Mathew
|
|
|
3 |
S1 |
p.
|
article
|
| 13 |
AI vs Humans: Predicting PE based on CTPA requests
|
Amin, Sheikh Ruhul
|
|
|
3 |
S1 |
p.
|
article
|
| 14 |
An Audit of the experience of Aidence lung nodule detection software users
|
Sylvester, George
|
|
|
3 |
S1 |
p.
|
article
|
| 15 |
An in-depth examination of the role of artificial intelligence in modern radiology
|
Rajaram, Akash
|
|
|
3 |
S1 |
p.
|
article
|
| 16 |
Application of image fusion to the transjugular intrahepatic portosystemic shunt (TIPS) procedure via Philips VesselNavigator software
|
Rafferty, Daniel
|
|
|
3 |
S1 |
p.
|
article
|
| 17 |
A Practical Consideration of the Ethical Challenges of AI in Healthcare – A Systematic Review
|
Akrami, Angelica
|
|
|
3 |
S1 |
p.
|
article
|
| 18 |
A real-world insight into the application of an artificial intelligence algorithm for the analysis of chest radiographs
|
Wong, Joshua
|
|
|
3 |
S1 |
p.
|
article
|
| 19 |
A Retrospective Study on the Performance of an AI Tool in Detecting Vertebral Fragility Fractures Across Multiple CT Protocols
|
Schramm, David Christopher
|
|
|
3 |
S1 |
p.
|
article
|
| 20 |
Artificial Intelligence (AI) in early triaging of referred patients with retinal disease – AI Solutions in Ophthalmic healthcare.
|
Obi, Ebube
|
|
|
3 |
S1 |
p.
|
article
|
| 21 |
Artificial Intelligence (AI) in radiology course: a deanery-wide experience
|
Varma, Piyush
|
|
|
3 |
S1 |
p.
|
article
|
| 22 |
Artificial intelligence (AI) tools enhance Hyper-IgE syndrome diagnosis by using human phenotype ontology (HPO) terminology rather than HPO ID
|
Kusters, Maaike
|
|
|
3 |
S1 |
p.
|
article
|
| 23 |
Artificial Intelligence for diagnostics in radiology practice: A rapid systematic scoping review
|
Lawrence, Rachel
|
|
|
3 |
S1 |
p.
|
article
|
| 24 |
Artificial intelligence for non-communicable diseases in South Asia
|
Flynn, Christian David
|
|
|
3 |
S1 |
p.
|
article
|
| 25 |
Artificial Intelligence governance and prioritisation tool. How do we responsibly pick the right applications for a District General Hospital?
|
Shah, Fatima
|
|
|
3 |
S1 |
p.
|
article
|
| 26 |
Artificial intelligence in oral dysplasia: advancing diagnostic and prognostic capabilities
|
Al-Najafi, Hadeel
|
|
|
3 |
S1 |
p.
|
article
|
| 27 |
A scoping review of radiomic techniques used to investigate cardiovascular disease
|
Badesha, Arshpreet Singh
|
|
|
3 |
S1 |
p.
|
article
|
| 28 |
Assessing the performance of a deep learning algorithm for detection of scaphoid fractures in paediatric radiographs
|
Evans, Samuel
|
|
|
3 |
S1 |
p.
|
article
|
| 29 |
A Survey on Knowledge and Attitudes of Radiology trainees towards Artificial Intelligence (AI) in Radiology in South India
|
Vanjare, Harshad Arvind
|
|
|
3 |
S1 |
p.
|
article
|
| 30 |
Can a diagnostic AI tool to read prostate cancer biopsies benefit the NHS?
|
Woods, Sam
|
|
|
3 |
S1 |
p.
|
article
|
| 31 |
Can artificial intelligence algorithm reliably distinguish normal chest X-rays from abnormal chest X-rays: a real-world post-marketing surveillance analysis in the United Kingdom
|
Das, Neelan
|
|
|
3 |
S1 |
p.
|
article
|
| 32 |
CDSS – An Interdisciplinary Perspective on the Statement of the Central Ethics Commission of the German Medical Association
|
Ziethmann, Paula
|
|
|
3 |
S1 |
p.
|
article
|
| 33 |
Clinical Evaluation of a Novel Deep Learning Algorithm for Automated Lymph Node Detection in Chest CT
|
Johnson, Alex
|
|
|
3 |
S1 |
p.
|
article
|
| 34 |
Clinical validation of the deep learning-based preoperative auxiliary planning system for liver cancer: a randomised controlled trial
|
Wang, Dawei
|
|
|
3 |
S1 |
p.
|
article
|
| 35 |
Convolutional Neural Network-Based Model for Improved Detection of Free Abdominal Air
|
Aydemir, Destina Gizem
|
|
|
3 |
S1 |
p.
|
article
|
| 36 |
Creating Conditions for Success: How to Successfully Implement CXR AI
|
Lampignano, Jesus Perdomo
|
|
|
3 |
S1 |
p.
|
article
|
| 37 |
Deep learning for Radiotherapy Autosegmentation Workflow (DRAW): System engineering and preliminary experience with an autosegmentation solution built using open-source software
|
Chakraborty, Santam
|
|
|
3 |
S1 |
p.
|
article
|
| 38 |
Detecting automation bias in AI autocontouring
|
Doolan, Paul
|
|
|
3 |
S1 |
p.
|
article
|
| 39 |
Developing a post-market surveillance audit methodology for an artificial intelligence as a medical device deployed autonomously in urgent suspected skin cancer pathways
|
Punjabi, Karan
|
|
|
3 |
S1 |
p.
|
article
|
| 40 |
Development of an in-house Artificial Intelligence auto-contouring model for target delineation in craniospinal irradiation
|
Ingram, Samuel
|
|
|
3 |
S1 |
p.
|
article
|
| 41 |
Does AI-Assisted Chest X-ray Interpretation Influence Clinical Management of Frontline Clinicians?
|
Shah, Ruchir
|
|
|
3 |
S1 |
p.
|
article
|
| 42 |
Does AI consider children as little adults? Performance of adult radiology AI tools applied to a paediatric population – a scoping review
|
Laborie, Lene
|
|
|
3 |
S1 |
p.
|
article
|
| 43 |
Elbowing in on Artificial Intelligence: Flexing ChatGPT-4’s Diagnostic Muscle in Paediatric Elbow Fractures
|
Sheng Phua, Jonathan Kia
|
|
|
3 |
S1 |
p.
|
article
|
| 44 |
Embedding professionalism and governance in AI integration: a strategic framework for healthcare leadership
|
Pal, Sanchita
|
|
|
3 |
S1 |
p.
|
article
|
| 45 |
Enhancing Cervical Lesion Detection through Instant Segmentation: Analysing Colposcopic Images with DeepLabv3+ Model
|
Sun, Yihung
|
|
|
3 |
S1 |
p.
|
article
|
| 46 |
Enhancing Early Skin Cancer Diagnosis: Ensemble Learning and Feature Engineering for Improved Detection
|
Pervez, Muhammad Tariq
|
|
|
3 |
S1 |
p.
|
article
|
| 47 |
Enhancing radiology assessments with human-centric AI: a pilot project in CT scan analysis
|
Srivastva, Manoj
|
|
|
3 |
S1 |
p.
|
article
|
| 48 |
Enhancing Reporting Efficiency and Accuracy for Degenerative Cervical Spine MRI with Deep Learning
|
Hallinan, James
|
|
|
3 |
S1 |
p.
|
article
|
| 49 |
Evaluating artificial intelligence for lung cancer detection on chest radiographs: multi-vendor comparison in a retrospective real-world UK population
|
Maiter, Ahmed
|
|
|
3 |
S1 |
p.
|
article
|
| 50 |
Evaluating large language models for automated TNM staging of lung cancer from radiology reports
|
Ap Emyr, Dafydd
|
|
|
3 |
S1 |
p.
|
article
|
| 51 |
Evaluating the Clinical Impact of AI-Driven Autonomous Chest Radiograph Reporting
|
Afzal, Farah
|
|
|
3 |
S1 |
p.
|
article
|
| 52 |
Evaluating the efficacy of an artificial intelligence (AI) auto-contouring algorithm for abdominal organs at risk (OARs) delineated during liver stereotactic body radiotherapy (SBRT) planning
|
Lee, Matthew
|
|
|
3 |
S1 |
p.
|
article
|
| 53 |
Evaluating the environmental sustainability of AI in radiology: a systematic review of current practice
|
Thomson, Rachel M.
|
|
|
3 |
S1 |
p.
|
article
|
| 54 |
Evaluating the role of artificial intelligence in identifying ventriculomegaly of clinical significance
|
Rastogi, Rajul
|
|
|
3 |
S1 |
p.
|
article
|
| 55 |
Evaluation of an AI tool to measure mammographic density for use in a FAST MRI trial
|
Gomes, Sandra
|
|
|
3 |
S1 |
p.
|
article
|
| 56 |
Evaluation of an Artificial Intelligence (AI)-powered Oncology Chatbot for Head and Neck Cancer Patients Undergoing Curative Intent Radiotherapy
|
Shams C, Salma
|
|
|
3 |
S1 |
p.
|
article
|
| 57 |
Evaluation of Brainomix e-CTA Software in the Diagnosis of Large-Vessel Occlusion Stroke
|
Tan, Alexander
|
|
|
3 |
S1 |
p.
|
article
|
| 58 |
Evaluation of Large Language Models with clinical guidance for vetting outpatient MRI lumbar spine referrals
|
Clackett, William
|
|
|
3 |
S1 |
p.
|
article
|
| 59 |
Exploring how different stakeholders view the use of artificial intelligence in MRI
|
Peplinski, Agnieszka
|
|
|
3 |
S1 |
p.
|
article
|
| 60 |
External Validation of a Commercially Available AI Tool for NGT Position Decision Support in the NHS
|
Bartsch, Anne-Marie
|
|
|
3 |
S1 |
p.
|
article
|
| 61 |
External Validation of an Artificial Intelligence Model for Paediatric Wrist Fracture Detection
|
Pauling, Cato
|
|
|
3 |
S1 |
p.
|
article
|
| 62 |
Generate-then-Classify: Can Large Language Models generate Better Summaries of Radiology Reports for Automated Report Classification?
|
Sabu, Sebin
|
|
|
3 |
S1 |
p.
|
article
|
| 63 |
Harnessing artificial intelligence for improving public health outcomes equitably – reality or rhetoric? A narrative review
|
Amadi-Livingstone, Chibuchi
|
|
|
3 |
S1 |
p.
|
article
|
| 64 |
Hospital-specific domain adaptation improves BERT-based models’ ability to classify neuroradiology reports
|
Agarwal, Siddharth
|
|
|
3 |
S1 |
p.
|
article
|
| 65 |
How could AI be used in postgraduate radiology training?
|
Curle, Jennifer
|
|
|
3 |
S1 |
p.
|
article
|
| 66 |
How does ChatGPT4omni perform in consenting for common orthopaedic and musculoskeletal interventional procedures?
|
Jagadeesha, Sushmitha Devihalli
|
|
|
3 |
S1 |
p.
|
article
|
| 67 |
How Is AI Transforming Radiography Reporting Workflows? An Analysis of Its Effectiveness and Practical Benefits
|
Khalil, M'Saraaz
|
|
|
3 |
S1 |
p.
|
article
|
| 68 |
‘How I would like AI used for paediatric imaging’ – parents' and carers’ perspective
|
Agarwal, Girija
|
|
|
3 |
S1 |
p.
|
article
|
| 69 |
Implementation of in-house pelvic radiotherapy auto-contouring in real-world clinical practice
|
Ribeiro, Luis
|
|
|
3 |
S1 |
p.
|
article
|
| 70 |
Implementing AI in MRI image reconstruction: a clinical QI project
|
Martin, Joe
|
|
|
3 |
S1 |
p.
|
article
|
| 71 |
Implementing AI in radiotherapy in Ireland
|
McClean, Brendan
|
|
|
3 |
S1 |
p.
|
article
|
| 72 |
Improving the generalisation of radiographic AI using automated data curation to mitigate shortcut learning
|
Selby, Ian
|
|
|
3 |
S1 |
p.
|
article
|
| 73 |
Improving the Pre-assessment Pathway for Cataract Surgery Using conversational clinical artificial intelligence
|
Jama, Khaled
|
|
|
3 |
S1 |
p.
|
article
|
| 74 |
Insights from real-world evaluation of an AI tool within the breast screening programme – a single-centre experience
|
Wignal, Alice
|
|
|
3 |
S1 |
p.
|
article
|
| 75 |
In the tutelage of ChatGPT: Assessing AI's capability to simplify radiology physics, a bilingual approach
|
Tyagi, Shardul
|
|
|
3 |
S1 |
p.
|
article
|
| 76 |
Large language models are well suited for data-mining free-text radiology referrals from multiple sources: Let chat-GPT do the heavy lifting for you
|
Montana, Ernest
|
|
|
3 |
S1 |
p.
|
article
|
| 77 |
Leveraging Evidence from Deep Learning Studies to Develop an Improved MRI-based Model for Breast Cancer Diagnosis
|
Abdullah, Kamarul Amin
|
|
|
3 |
S1 |
p.
|
article
|
| 78 |
Low-dose computed tomography with deep learning reconstruction versus standard-dose computed tomography for malignant liver tumours: A systematic review and meta-analysis
|
Ayogu, Chukwudi
|
|
|
3 |
S1 |
p.
|
article
|
| 79 |
Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review
|
Daga, Karan
|
|
|
3 |
S1 |
p.
|
article
|
| 80 |
Machine Learning Models Using Extended Clinical Variables to Predict Real-World Head and Neck Cancer Radiotherapy Toxicity
|
Young, Thomas
|
|
|
3 |
S1 |
p.
|
article
|
| 81 |
Navigating challenges in AI-assisted knee MRI reporting: a case-based study
|
Alimohamed, Kasim
|
|
|
3 |
S1 |
p.
|
article
|
| 82 |
Next-generation virtual and augmented reality in surgical education: a narrative review
|
Sheik-Ali, Sharaf
|
|
|
3 |
S1 |
p.
|
article
|
| 83 |
ODELIA – European swarm learning project for AI in breast cancer MRI
|
Saldanha, Oliver
|
|
|
3 |
S1 |
p.
|
article
|
| 84 |
On becoming a computer-assisted language learning (CALL) teacher – professional growth after faculty capacity enhancement training: female teachers’ narratives
|
Sharma, Mani Ram
|
|
|
3 |
S1 |
p.
|
article
|
| 85 |
On becoming a computer-assisted language learning (CALL) teacher – professional growth after faculty capacity enhancement training: female teachers’ narratives
|
Sharma, Mani Ram
|
|
|
3 |
S1 |
p.
|
article
|
| 86 |
Optimising a real-world queuing problem: triaging MRI brain scans using imperfect artificial intelligence models
|
Lyu, Zezheng
|
|
|
3 |
S1 |
p.
|
article
|
| 87 |
Performance of AI in fracture detection: An overview of reviews
|
O'Hanlon, Ciarán
|
|
|
3 |
S1 |
p.
|
article
|
| 88 |
Personalising education for radiologists using AI: a breast imaging case study
|
Gandomkar, Ziba
|
|
|
3 |
S1 |
p.
|
article
|
| 89 |
Post-market surveillance of artificial intelligence fracture detection in live clinical operational use in the UK
|
Braich, Amanjeet
|
|
|
3 |
S1 |
p.
|
article
|
| 90 |
Precision of automated cardiac chambers and great vessel volume segmentation in difficult cases using an open-source full-body segmentation model
|
Sommerfeld, Lisa
|
|
|
3 |
S1 |
p.
|
article
|
| 91 |
Procedure for development and evaluation of exergame for hand rehabilitation using Leap Motion Controller: a feasibility pilot study
|
Masood, Sabeen
|
|
|
3 |
S1 |
p.
|
article
|
| 92 |
Prognosticating epilepsy by artificial intelligence-based brain volumetric analysis
|
Rastogi, Rajul
|
|
|
3 |
S1 |
p.
|
article
|
| 93 |
Prognostication of psychosis based on artificial intelligence volume analysis of MRI brain
|
Rastogi, Rajul
|
|
|
3 |
S1 |
p.
|
article
|
| 94 |
Proof of Concept for an AI-Powered Pipeline in Paediatric Radiology
|
Rigny, Louise
|
|
|
3 |
S1 |
p.
|
article
|
| 95 |
Radiogenomic AI model predicts immune status in IDH wildtype glioblastoma: PRECISE-GBM study
|
Ghimire, Prajwal
|
|
|
3 |
S1 |
p.
|
article
|
| 96 |
Radiological research trends in the use of convolutional neural networks for breast cancer detection, diagnosis and classification: A bibliometric analysis of the 100 most-cited articles
|
Lee Li, Ka
|
|
|
3 |
S1 |
p.
|
article
|
| 97 |
Radiology staff perspectives on the use of artificial intelligence in medical imaging
|
Agarwal, Girija
|
|
|
3 |
S1 |
p.
|
article
|
| 98 |
R-AI-diographers: exploring the changing professional role and identity of radiographers in Europe in the era of artificial intelligence
|
Walsh, Gemma
|
|
|
3 |
S1 |
p.
|
article
|
| 99 |
RapidAI and Annalise.ai for Detection of Subarachnoid Haemorrhage in Head CT Scans
|
Wei, Jeremy Lok
|
|
|
3 |
S1 |
p.
|
article
|
| 100 |
Rapid mixed-method evaluation of implementing Artificial Intelligence in chest diagnostics for lung disease
|
Ramsay, Angus
|
|
|
3 |
S1 |
p.
|
article
|
| 101 |
Real-world evaluation of a commercial machine learning-based tool for the longitudinal analysis of multiple sclerosis plaques on brain MRI
|
Nesar, Samia
|
|
|
3 |
S1 |
p.
|
article
|
| 102 |
Retrospective assessment of lung cancer detection ability of chest radiograph AI software
|
Giucca, Alice
|
|
|
3 |
S1 |
p.
|
article
|
| 103 |
Revolutionising Competency-Based Medical Education with AI
|
Lay, David
|
|
|
3 |
S1 |
p.
|
article
|
| 104 |
Risk Identification and Relapse Prediction in Lung Adenocarcinoma (LUAD)
|
Khatri, Radhika
|
|
|
3 |
S1 |
p.
|
article
|
| 105 |
Risk prediction and quantification of cardiovascular disease using machine learning: advancing early diagnosis with clinical insights
|
Kapse, Vijay Suryakant
|
|
|
3 |
S1 |
p.
|
article
|
| 106 |
Role of artificial intelligence in prognosticating neurodegenerative diseases
|
Rastogi, Rajul
|
|
|
3 |
S1 |
p.
|
article
|
| 107 |
Semantic segmentation for brain volumetry: a deep learning approach using U-Net on T1- and T2-weighted magnetic resonance imaging (MRI) scans
|
Torik, Iman Attackpour
|
|
|
3 |
S1 |
p.
|
article
|
| 108 |
Shaping AI education in imaging and harnessing learning opportunities: initial perspectives from Radiology trainees
|
Sawer, Alex
|
|
|
3 |
S1 |
p.
|
article
|
| 109 |
Smart reporting: ChatGPT vs radiologists in MSK knee MRI analysis
|
Rustagi, Vikash
|
|
|
3 |
S1 |
p.
|
article
|
| 110 |
SpeedyAnnotate: An Intuitive and Open-Source Tool for Efficient Image Annotation and Quality Comparison
|
Selby, Ian
|
|
|
3 |
S1 |
p.
|
article
|
| 111 |
Synthesised low-dose clinical screening mammograms: an evaluation of suitability for AI training
|
Worthy, Anna
|
|
|
3 |
S1 |
p.
|
article
|
| 112 |
Technology-enhanced learning with PGVLE: standardising neuroradiology training through the NODE initiative
|
Menon, Nitin
|
|
|
3 |
S1 |
p.
|
article
|
| 113 |
The ability of artificial intelligence to correctly identify acute haemorrhage in A&E patients on a non-contrast CT head: a review of false positives in Brainomix in a single centre
|
Hameed, Aisha
|
|
|
3 |
S1 |
p.
|
article
|
| 114 |
The ability of artificial intelligence to correctly identify extra and intra-axial intracranial haemorrhage in A&E patients on non-contrast CT head: a single-centre review of Brainomix
|
Hameed, Aisha
|
|
|
3 |
S1 |
p.
|
article
|
| 115 |
The impact of AI-driven remote patient monitoring on cancer care: a systematic review
|
Aziz, Fayha
|
|
|
3 |
S1 |
p.
|
article
|
| 116 |
The Impact of Artificial Intelligence on the Detection of Incidental and Symptomatic Pulmonary Emboli: A Review of 11,000 CT studies
|
O'Herlihy, Fergus
|
|
|
3 |
S1 |
p.
|
article
|
| 117 |
The variability of classification labels is an important barrier to the effective comparison of artificial intelligence software between vendors
|
Maiter, Ahmed
|
|
|
3 |
S1 |
p.
|
article
|
| 118 |
Towards improved detection of bone metastases on CT scans with the use of a novel artificial intelligence software
|
Regnard, Nor-eddine
|
|
|
3 |
S1 |
p.
|
article
|
| 119 |
Transforming the diagnosis of Developmental dysplasia of the hip: Validation and Implementation modules of an AI-supported handheld ultrasound scanner.
|
Henderson, Joyce
|
|
|
3 |
S1 |
p.
|
article
|
| 120 |
Understanding current patient attitudes to AI: results from an 18-item questionnaire
|
Maclean, Rory
|
|
|
3 |
S1 |
p.
|
article
|
| 121 |
Use of NLP techniques to extract and codify radiologist opinions from radiology reports: an ‘at scale’ strategy for delivering post-market surveillance of AI tools in clinical practice
|
Fu, Howell
|
|
|
3 |
S1 |
p.
|
article
|
| 122 |
Utilisation of Brainomix in suspected stroke patients
|
Obasa, Afolabi
|
|
|
3 |
S1 |
p.
|
article
|
| 123 |
Virtual readouts: radiologists should embrace the change
|
Allam, Khalid
|
|
|
3 |
S1 |
p.
|
article
|
| 124 |
What is the economic value of an AI tool for the triage of skin cancer in the UK?
|
Woods, Sam
|
|
|
3 |
S1 |
p.
|
article
|
| 125 |
Which paediatric fractures lack research evidence regarding their diagnosis using artificial intelligence? A systematic review
|
Evans, Samuel
|
|
|
3 |
S1 |
p.
|
article
|
Journal description