Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Editorial
. 2023 Sep;308(3):e232308.
doi: 10.1148/radiol.232308.

Foundation AI Models and Data Extraction from Unlabeled Radiology Reports: Navigating Uncharted Territory

Affiliations
Editorial

Foundation AI Models and Data Extraction from Unlabeled Radiology Reports: Navigating Uncharted Territory

Nima Hafezi-Nejad et al. Radiology. 2023 Sep.
No abstract available

PubMed Disclaimer

Conflict of interest statement

Disclosures of conflicts of interest: N.H.N. No relevant relationships. P.T. Grants from the National Heart, Lung and Blood Institute, National Cancer Institute, and the American Heart Association; consulting fees from Boston Scientific; leadership or fiduciary role in Pneumonix.

Figures

Nima Hafezi-Nejad, MD, MPH, is an assistant professor of vascular and
interventional radiology in the Russell H. Morgan Department of Radiology and
Radiological Sciences at the Johns Hopkins University School of Medicine. His
interest is in the application of quantitative methods in prediction of
health-related outcomes. He has studied the epidemiology of noncommunicable
disorders and comparative effectiveness of image-guided
interventions.
Nima Hafezi-Nejad, MD, MPH, is an assistant professor of vascular and interventional radiology in the Russell H. Morgan Department of Radiology and Radiological Sciences at the Johns Hopkins University School of Medicine. His interest is in the application of quantitative methods in prediction of health-related outcomes. He has studied the epidemiology of noncommunicable disorders and comparative effectiveness of image-guided interventions.
Premal Trivedi, MD, MSE, is an associate professor and interim chief of
interventional radiology at the University of Colorado Anschutz Medical Center.
He is the director of health services research and focuses on the cost and
clinical outcomes of image guided procedures and designing solutions to optimize
care at the population health level.
Premal Trivedi, MD, MSE, is an associate professor and interim chief of interventional radiology at the University of Colorado Anschutz Medical Center. He is the director of health services research and focuses on the cost and clinical outcomes of image guided procedures and designing solutions to optimize care at the population health level.

Comment on

Similar articles

Cited by

References

    1. Kelly BS , Judge C , Bollard SM , et al. . Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE) . Eur Radiol 2022. ; 32 ( 11 ): 7998 – 8007 . [Published correction appears in Eur Radiol 2022;32(11):8054.] - PMC - PubMed
    1. Hosny A , Parmar C , Quackenbush J , Schwartz LH , Aerts HJWL . Artificial intelligence in radiology . Nat Rev Cancer 2018. ; 18 ( 8 ): 500 – 510 . - PMC - PubMed
    1. Pan I , Baird GL , Mutasa S , et al. . Rethinking Greulich and Pyle: A Deep Learning Approach to Pediatric Bone Age Assessment Using Pediatric Trauma Hand Radiographs . Radiol Artif Intell 2020. ; 2 ( 4 ): e190198 . - PMC - PubMed
    1. Pons E , Braun LM , Hunink MG , Kors JA . Natural Language Processing in Radiology: A Systematic Review . Radiology 2016. ; 279 ( 2 ): 329 – 343 . - PubMed
    1. Wiggins WF , Tejani AS . On the Opportunities and Risks of Foundation Models for Natural Language Processing in Radiology . Radiol Artif Intell 2022. ; 4 ( 4 ): e220119 . - PMC - PubMed

LinkOut - more resources