Article CommentaryWHITE PAPER
Critical Appraisal of Artificial Intelligence–Enabled Imaging Tools Using the Levels of Evidence System
N. Pham, V. Hill, A. Rauschecker, Y. Lui, S. Niogi, C.G. Fillipi, P. Chang, G. Zaharchuk and M. Wintermark
American Journal of Neuroradiology April 2023, DOI: https://doi.org/10.3174/ajnr.A7850
N. Pham
aFrom the Department of Radiology (N.P., G.Z.), Stanford School of Medicine, Palo Alto, California
V. Hill
bDepartment of Radiology (V.H.), Northwestern University Feinberg School of Medicine, Chicago, Illinois
A. Rauschecker
cDepartment of Radiology (A.R.), University of California, San Francisco, San Francisco, California
Y. Lui
dDepartment of Radiology (Y.L.), NYU Grossman School of Medicine, New York, New York
S. Niogi
eDepartment of Radiology (S.N.), Weill Cornell Medicine, New York, New York
C.G. Fillipi
fDepartment of Radiology (C.G.F.), Tufts University School of Medicine, Boston, Massachusetts
P. Chang
gDepartment of Radiology (P.C.), University of California, Irvine, Irvine, California
G. Zaharchuk
aFrom the Department of Radiology (N.P., G.Z.), Stanford School of Medicine, Palo Alto, California
M. Wintermark
hDepartment of Neuroradiology (M.W.), The University of Texas MD Anderson Cancer Center, Houston, Texas

References
- 1.↵
- Zaharchuk G,
- Gong E,
- Wintermark M, et al
- 2.↵
- Lui YW,
- Chang PD,
- Zaharchuk G, et al
- 3.↵
- Bohr A,
- Memarzadeh K
- 4.↵
- 5.↵Artificial intelligence and machine learning (AI/ML)-enabled medical devices. October 5, 2022. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices. Accessed March 5, 2023
- 6.↵
- 7.↵
- 8.↵
- 9.↵
- 10.↵
- 11.↵
- 12.↵
- 13.↵
- 14.↵
- 15.↵
- 16.↵
- 17.↵
- 18.↵Radiology: Artificial intelligence. Checklist for Artificial Intelligence in Medical Imaging (CLAIM). https://pubs.rsna.org/page/ai/claim?doi=10.1148%2Fryai&publicationCode=ai. Accessed March 2, 2023
- 19.↵
- Liu X,
- Rivera SC,
- Moher D, et al
- 20.↵
- 21.↵
- Jha A,
- Bradshaw T,
- Buvat I, et al
- 22.↵
- 23.↵
- 24.↵
- 25.↵
- 26.↵
- 27.↵
- 28.↵
- 29.↵
- 30.↵
- 31.↵
- 32.↵
- 33.↵
- 34.↵
- 35.↵
- 36.↵
- 37.↵
- 38.↵
- Vollmer S,
- Mateen BA,
- Bohner G, et al
- 39.↵
- 40.↵
- 41.↵
- 42.↵
- Genereaux B,
- O'Donnell K,
- Bialecki B, et al
- 43.↵
- 44.↵
- 45.↵
- Porter ME
- 46.↵
- Brady AP,
- Visser J,
- Frija G, et al
- 47.↵
- 48.↵
- 49.↵
- 50.↵
- 51.↵
- Soun JE,
- Chow DS,
- Nagamine M, et al
- 52.↵
- 53.↵
- 54.↵
- 55.
- 56.
Advertisement
N. Pham, V. Hill, A. Rauschecker, Y. Lui, S. Niogi, C.G. Fillipi, P. Chang, G. Zaharchuk, M. Wintermark
Critical Appraisal of Artificial Intelligence–Enabled Imaging Tools Using the Levels of Evidence System
American Journal of Neuroradiology Apr 2023, DOI: 10.3174/ajnr.A7850
0 Responses
Jump to section
Related Articles
- No related articles found.
Cited By...
- Outcomes of Radiologist Recommendations for Temporal Bone CT to Assess Superior Semicircular Canal Dehiscence on Temporal Bone MRI
- Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs
- Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study
- Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: a multimodal approach integrating clinical and deep imaging features
- A Siamese U-Transformer for change detection on MRI brain for multiple sclerosis, a model development and external validation study
- A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS
This article has not yet been cited by articles in journals that are participating in Crossref Cited-by Linking.
More in this TOC Section
Similar Articles
Advertisement