References

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Rajpurkar, Pranav, and Matthew P. Lungren. "The Current and Future State of AI Interpretation of Medical Images." New England Journal of Medicine 388, no. 21 (2023): 1981-1990. DOI https://doi.org/10.1056/NEJMra2301725

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Gulshan, Varun, Lily Peng, Marc Coram, Martin C. Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan et al. "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs." Jama 316, no. 22 (2016): 2402-2410. google citations: https://scholar.google.com/scholar?cites=16083985573643781536&as_sdt=2005&sciodt=0,5&hl=en PDF paper: https://scholar.google.com/scholar?cluster=16083985573643781536&hl=en&as_sdt=2005&sciodt=0,5

• Grading Tool

  For the development set, between 3-7 grades were obtained for each image. The graders for the development set were U.S. licensed ophthalmologists or ophthalmology trainees in their last year of residency (PGY-4). All graders were asked to grade a 19-image test set prior to starting grading to ensure that they were proficient in reading diabetic retinopathy fundus images and were monitored for inter-grader and intra-grader consistency (details below Grading Quality Control section). Figure 1. Screenshot of First Screen of Grading Tool Which Asks Graders to Assess Image Quality > https://cdn.jamanetwork.com/ama/content_public/journal/jama/935924/joi160132supp1_prod.pdf

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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 Digit. Med. 3, 118 (2020). https://doi.org/10.1038/s41746-020-00324-0

https://medicalfuturist.com/fda-approved-ai-based-algorithms/

FDA Publishes Updated List Of 521 Authorized AI Enabled Medical Devices https://www.linkedin.com/pulse/fda-publishes-updated-list-521-authorized-aiml-margaretta-colangelo/

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Wang, Tonghe, Yang Lei, Yabo Fu, Jacob F. Wynne, Walter J. Curran, Tian Liu, and Xiaofeng Yang. "A review on medical imaging synthesis using deep learning and its clinical applications." Journal of applied clinical medical physics 22, no. 1 (2021): 11-36. google-citations: https://scholar.google.com/scholar?cites=15441695928760038188&as_sdt=2005&sciodt=0,5&hl=en

"Suboptimal demographic diversity may reduce the robustness and generalizability of any model. Most studies reviewed here trained models using data from a single institution with a single scanner. Model performance across hardware of several models or manufacturers, wherein image characteristics cannot be exactly matched, is an important consideration due to frequent hardware replacement and upgrade in the modern clinical setting. Boni et al. recently presented a proof‐of‐concept study that predicted synthetic images of one clinical site using a model trained on data from two other sites and demonstrated clinically acceptable results.142 Further studies could include datasets from multiple centers and adopt a leave‐one‐center‐out training and/or test strategy in order to validate the consistency and robustness of the network" 142: Boni, Kévin ND Brou, John Klein, Ludovic Vanquin, Antoine Wagner, Thomas Lacornerie, David Pasquier, and Nick Reynaert. "MR to CT synthesis with multicenter data in the pelvic area using a conditional generative adversarial network." Physics in Medicine & Biology 65, no. 7 (2020): 075002. https://scholar.google.com/scholar?cites=16963811398294551586&as_sdt=2005&sciodt=0,5&hl=en PDF: https://www.researchgate.net/profile/John-Klein-2/publication/339243292_MR_to_CT_synthesis_with_multicenter_data_in_the_pelvic_era_using_a_conditional_generative_adversarial_network/links/5e47b573299bf1cdb92b91da/MR-to-CT-synthesis-with-multicenter-data-in-the-pelvic-era-using-a-conditional-generative-adversarial-network.pdf

[mxochicale]

Skandarani, Youssef, Pierre-Marc Jodoin, and Alain Lalande. "Gans for medical image synthesis: An empirical study." Journal of Imaging 9, no. 3 (2023): 69. https://www.mdpi.com/2313-433X/9/3/69 https://scholar.google.com/scholar?cites=5044961699826960980&as_sdt=2005&sciodt=0,5&hl=en

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Tejani, Ali S., Michail E. Klontzas, Anthony A. Gatti, John T. Mongan, Linda Moy, Seong Ho Park, Charles E. Kahn Jr, and CLAIM 2024 Update Panel. "Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update." Radiology: Artificial Intelligence (2024): e240300. https://pubs.rsna.org/doi/10.1148/ryai.240300