Research graph
References from AI-RADS: A Framework for Assessment of Artificial Intelligence Output in Radiology. Local targets link to admitted publications; unresolved targets remain external evidence.
deep learning super-resolution reconstruction for fast and motion-robust T2-weighted prostate MRI
10.1148/radiol.230427 · 2023 · External reference
Use of AI in cardiac CT and MRI: a scientific statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA
10.1148/radiol.240516 · 2025 · External reference
Accuracy of fully automated and human-assisted artificial intelligence-based CT quantification of pleural effusion changes after thoracentesis
10.1148/ryai.240215 · 2025 · External reference
Using AI to improve radiologist performance in detection of abnormalities on chest radiographs
10.1148/radiol.230860 · 2023 · External reference
Artificial intelligence and interstitial lung disease: diagnosis and prognosis
10.1097/rli.0000000000000974 · 2023 · External reference
Artificial intelligence outcome prediction in neonates with encephalopathy (AI-OPiNE)
10.1148/ryai.240076 · 2024 · External reference
Performance of lung cancer prediction models for screening-detected, incidental, and biopsied pulmonary nodules
10.1148/ryai.230506 · 2025 · External reference
FDA-approved artificial intelligence and machine learning (AI/ML)-enabled medical devices: an updated landscape
2024 · External reference
A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare
2024 · External reference
Artificial intelligence and machine learning in radiology: current state and considerations for routine clinical implementation
10.1097/rli.0000000000000673 · 2020 · External reference
Are current clinical studies on artificial intelligence-based medical devices comprehensive enough to support a full health technology assessment? A systematic review
10.1016/j.artmed.2023.102547 · 2023 · External reference
Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)
10.1007/s00330-022-08784-6 · 2022 · External reference
Pitfalls and best practices in evaluation of ai algorithmic biases in radiology
10.1148/radiol.241674 · 2025 · External reference
Advancing medical imaging research through standardization: the path to rapid development, rigorous validation, and robust reproducibility
10.1097/rli.0000000000001106 · 2025 · External reference
Retrieval-augmented generation with large language models in radiology: from theory to practice
10.1148/ryai.240790 · 2025 · External reference
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 update
10.1148/ryai.240300 · 2024 · External reference
To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)
10.1007/s00330-020-07684-x · 2021 · External reference
Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. a multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA
2024 · External reference
Implementation of clinical artificial intelligence in radiology: who decides and how?
10.1148/radiol.212151 · 2022 · External reference
FDA review of radiologic AI algorithms: process and challenges
10.1148/radiol.230242 · 2024 · External reference
Openness and transparency in the evaluation of bias in artificial intelligence
10.1148/radiol.222263 · 2023 · External reference
PI-RADS versions 2 and 2.1: interobserver agreement and diagnostic performance in peripheral and transition zone lesions among six radiologists
10.2214/ajr.20.24199 · 2021 · External reference
BI-RADS lexicon for US and mammography: interobserver variability and positive predictive value
10.1148/radiol.2392042127 · 2006 · External reference
Interobserver variability between breast imagers using the fifth edition of the BI-RADS MRI lexicon
10.2214/ajr.14.13047 · 2015 · External reference