Rationale and Objectives
Materials and Methods
Results
Conclusion
Key Words
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Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ digital medicine. 2020;3:1-8.
Chacon, A, Plasencia, JT, Avila, G, et al. (2019). A deep learning model to aid in detection of pneumothorax via CXR: a retrospective cohort analysis of the NIH-based CXR dataset. Chest, 156(4), A917-A918.
Gawlitza, J, Sturm, T, Spohrer, K, et al. (2019). Predicting pulmonary function testing from quantified computed tomography using machine learning algorithms in patients with COPD. Diagnostics, 9(1), 33.
Uthoff, J, Stephens, MJ, Newell Jr, JD, et al. (2019). Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT. Med Phys, 46(7), 3207-3216.
Ji, Y, Li, H, Edwards, AV, et al. (2019). Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution. Cancer Imaging, 19(1), 1-11.
Mushtaq, J, Pennella, R, Lavalle, S, et al. (2021). Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients. Eur Radiol, 31(3), 1770-1779.
Sharma, A, Rani, S, & Gupta, D (2020). Artificial intelligence-based classification of chest X-ray images into COVID-19 and other infectious diseases. Int J Biomed Imaging, 2020;2020:8889023. Published 2020 Oct 6. https://doi.org/10.1155/2020/8889023
Belfiore, MP, Urraro, F, Grassi, R, et al. (2020). Artificial intelligence to codify lung CT in Covid-19 patients. La radiologia medica, 125(5), 500-504.
Li, L, Qin, L, Xu, Z, et al. (2020). Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology, 296(2), E65-E71.
Harmon, SA, Sanford, TH, Xu, S, et al. (2020). Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat Commun, 11(1), 1-7.
Roberts M, Driggs D, Thorpe M, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell. 2021;3:199-217.
FDA Cleared AI Algorithms. American College of Radiology Data Science Institute. Available from: https://models.acrdsi.org/ [Accessed May 10, 2021]
Available from: accessed at https://ncihub.org/groups/eedapstudies/wiki/DeviceAdviceAIMLImaging/File:J.A.Segui.ACR.Informatics.2019.Slides.FINAL.pdf on August 10, 2021
Available from: https://www.fda.gov/media/135712/download May 10, 2021
Medical Futurist. Available from: https://medicalfuturist.com/fda-approved-ai-based-algorithms/ [Accessed May 10, 2021]
DeGrave AJ, Janizek JD, Lee SI. AI for radiographic COVID-19 detection selects shortcuts over signal. Preprint. medRxiv. 2020;2020.09.13.20193565. Published 2020 Oct 7. https://doi.org/10.1101/2020.09.13.20193565
Wu E, Wu K, Daneshjou R, et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med. 2021;27:582-4.
van Leeuwen KG, Schalekamp S, Rutten MJ, et al. Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol. 2021 Jun;31(6):3797-804.
Dreyer KJ, Allen B, Wald C. Real-World Surveillance of FDA-cleared AI models: Rationale and Logistics. JACR (in press)