Clark, K. et al. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging 26, 1045-1057 (2013).
Article PubMed PubMed Central Google Scholar
Dong, J. et al. Clinical trials for artificial intelligence in cancer diagnosis: a cross-sectional study of registered trials in ClinicalTrials. gov. Frontiers in oncology 10, 1629 (2020).
U.S. Food & Drug Administration & Center for Devices and Radiological Health. Guidance for Industry: General/Specific Intended Use. (1998).
Sahiner, B., Chen, W., Samala, R. K. & Petrick, N. Data drift in medical machine learning: implications and potential remedies. The British Journal of Radiology 96, 20220878 (2023).
Article PubMed PubMed Central Google Scholar
Viergever, R. F. & Hendriks, T. C. The 10 largest public and philanthropic funders of health research in the world: what they fund and how they distribute their funds. Health research policy and systems 14, 1-15 (2016).
National Institutes of Health. 2023 NIH Data Management and Sharing Policy, <https://sharing.nih.gov/data-management-and-sharing-policy> (2023).
Faheem, H. & Dutta, S. Artificial Intelligence Failure at IBM'Watson for Oncology'. IUP Journal of Knowledge Management 21, 47-75 (2023).
Sandeep Konam. Where did IBM go wrong with Watson Health?, <https://qz.com/2129025/where-did-ibm-go-wrong-with-watson-health> (2022).
U.S. Food & Drug Administration. Using Artificial Intelligence & Machine Learning in the Development of Drug & Biological Products: Discussion Paper and Request for Feedback. (2023).
UK Biobank. UK Biobank Data Showcase, <https://www.ukbiobank.ac.uk/enable-your-research/about-our-data> (2024).
Tsai, E. et al. Data from Medical Imaging Data Resource Center (MIDRC) - RSNA International COVID Radiology Database (RICORD) Release 1c - Chest x-ray, Covid+ (MIDRC-RICORD-1C). The Cancer Imaging Archive. (2021). https://doi.org/10.7937/91ah-v663
Glynn, P. & Greenland, P. Contributions of the UK biobank high impact papers in the era of precision medicine. European Journal of Epidemiology 35, 5-10 (2020).
U. S. Food and Drug Administration. Artificial Intelligence Program: Research on AI/ML-Based Medical Devices, <https://www.fda.gov/medical-devices/medical-device-regulatory-science-research-programs-conducted-osel/artificial-intelligence-program-research-aiml-based-medical-devices> (2023).
Agency for Healthcare Research and Quality Rockville MD. Compendium of U.S. Health Systems, 2022, <https://www.ahrq.gov/chsp/data-resources/compendium-2022.html> (2022).
Sammer, M. B. et al. Use of artificial intelligence in radiology: impact on pediatric patients, a white paper from the ACR pediatric AI workgroup. Journal of the American College of Radiology 20, 730-737 (2023).
Sunoqrot, M. R., Saha, A., Hosseinzadeh, M., Elschot, M. & Huisman, H. Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges. European radiology experimental 6, 35 (2022).
Article PubMed PubMed Central Google Scholar
U. S. Food and Drug Administration. Center for Devices and Radiological Health, 2022–2025 Strategic Priorities. (U. S. Food and Drug Administration,, 2022).
U.S. Food & Drug Administration. Pulse Oximeters - Premarket Notification Submissions [510(k)s] Guidance for Industry and Food and Drug Administration Staff. (2013).
Wolf, R. M., Channa, R., Abramoff, M. D. & Lehmann, H. P. Cost-effectiveness of autonomous point-of-care diabetic retinopathy screening for pediatric patients with diabetes. JAMA ophthalmology 138, 1063-1069 (2020).
U.S. Food & Drug Administration. Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data - Premarket Notification [510(k)] Submissions: Guidance for Industry and Food and Drug Administration Staff. (2012).
U.S. Food & Drug Administration. Clinical Performance Assessment: Considerations for Computer-Assisted Detection Devices Applied to Radiology Images and Radiology Device Data in Premarket Notification (510(k)) Submissions: Guidance for Industry and FDA Staff. (2020).
Davis, S. E., Lasko, T. A., Chen, G., Siew, E. D. & Matheny, M. E. Calibration drift in regression and machine learning models for acute kidney injury. Journal of the American Medical Informatics Association 24, 1052-1061 (2017).
Article PubMed PubMed Central Google Scholar
U.S. Food & Drug Administration. FDA and Industry Actions on Premarket Notification (510(k)) Submissions: Effect on FDA Review Clock and Goals: Guidance for Industry and Food and Drug Administration Staff. (2022).
Elfer, K. et al. Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models. Modern Pathology 37, 100439 (2024).
Shah, N. H. et al. A Nationwide Network of Health AI Assurance Laboratories. JAMA 331, 245-249 (2024).
Sizikova, E. et al. Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses. Advances in Neural Information Processing Systems 36 (2024).
Pinaya, W. H. et al. Generative AI for medical imaging: extending the MONAI framework. arXiv preprint arXiv:2307.15208 (2023).
Tsai, E. B. et al. Medical Imaging Data Resource Center (MIDRC) - RSNA International COVID Open Research Database (RICORD) Release 1b - Chest CT Covid- . The Cancer Imaging Archive (2021). https://doi.org/10.7937/31V8-4A40
Jeong, J. J. et al. The EMory BrEast imaging Dataset (EMBED): A racially diverse, granular dataset of 3.4 million screening and diagnostic mammographic images. Radiology: Artificial Intelligence 5, e220047 (2023).
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