Yala, Adam, et al. “Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model.” Journal of Clinical Oncology 40, no. 33 (2022): 3850–60. https://doi.org/10.1200/JCO.21.01337.
A large external validation showing Mirai’s 1–5-year risk prediction held across seven health systems with C-indices ~0.75–0.84. 2. MIT Jameel Clinic. “Mirai.” Accessed October 2, 2025.
https://jclinic.mit.edu/mirai/. Public page reporting Mirai’s scale (2M+ mammograms; 72 hospitals; 22 countries) and intended clinical use. 3. National Academy of Medicine.
“Can AI Predict Breast Cancer? How a Scientist’s Personal Journey Led to an AI Model.” June 12, 2025. https://nam.edu/news-and-insights/can-ai-predict-breast-cancer/. Barzilay explains Mirai’s core idea—“the tissue itself imprints a lot of information.” 4. Mikhael, Peter G., et al.
“Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk from a Single Low-Dose Chest CT.” Journal of Clinical Oncology 41, no. 13 (2023): 2458–69. https://doi.org/10.1200/JCO.22.01345. Introduces Sybil and validates 1–6-year lung-cancer risk prediction.
5. UC Davis Health. “New AI Technology Helps Physicians Quickly Identify Stroke.” February 1, 2024. https://health.ucdavis.edu/news/headlines/new-ai-technology-helps-physicians-quickly-identify-stroke/2024/02.
Local rollout with Dr. Kwan Ng’s quotes on prioritization and speed. 6. U.S.
Food and Drug Administration. “IDx-DR — DEN180001: Decision Summary.” 2018. https://www.accessdata.fda.gov/cdrh_docs/reviews/DEN180001.pdf. FDA’s official De Novo review for the first autonomous diagnostic AI.
7. Repici, Alessandro, et al. “Efficacy of Real-Time Computer-Aided Detection During Colonoscopy.” Gastroenterology 159, no. 2 (2020): 512–20.e7.
https://doi.org/10.1053/j.gastro.2020.04.062. Multicenter RCT showing adenoma detection rate gains with CADe. 8. U.S.
Food and Drug Administration. “Paige Prostate — De Novo Decision Letter (DEN200080).” September 21, 2021. https://www.accessdata.fda.gov/cdrh_docs/pdf20/DEN200080.pdf. FDA authorization for the first AI tool in diagnostic pathology.
9. Wong, Andrew, et al. “External Validation of a Widely Implemented Sepsis Prediction Model.” JAMA Internal Medicine 181, no. 8 (2021): 1065–70.
https://doi.org/10.1001/jamainternmed.2021.2626. Shows Epic’s sepsis model performed poorly in real-world testing. 10. Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil Mullainathan.
“Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations.” Science 366, no. 6464 (2019): 447–53. https://doi.org/10.1126/science.aax2342. Demonstrates bias when cost is used as a proxy for health need.
11. Garde, Damian. “IBM’s Watson Recommended ‘Unsafe and Incorrect’ Cancer Treatments.” STAT , July 25, 2018. https://www.statnews.com/2018/07/25/ibm-watson-recommended-unsafe-incorrect-treatments/.
Reporting on internal documents detailing Watson for Oncology’s failures. 12. U.S. Food and Drug Administration.
“FDA Issues Comprehensive Draft Guidance for Developers of AI-Enabled Medical Devices.” January 6, 2025. https://www.fda.gov/news-events/press-announcements/fda-issues-comprehensive-draft-guidance-developers-artificial-intelligence-enabled-medical-devices. Notes “more than 1,000” AI-enabled devices have been authorized and outlines lifecycle oversight. 13.
Stokes, Jonathan M., et al. “A Deep Learning Approach to Antibiotic Discovery.” Cell 180, no. 4 (2020): 688–702.e13. https://doi.org/10.1016/j.cell.2020.01.021.
Reports the AI-driven discovery of the antibiotic halicin and in-vivo efficacy. 14. University Hospitals Sussex NHS Foundation Trust. “‘I Just Feel So Lucky’ – AI Helping Women to Have Breast Cancer Detected Earlier.” November 7, 2024.
https://www.uhsussex.nhs.uk/news/i-just-feel-so-lucky-ai-helping-women-to-have-breast-cancer-detected-earlier/. Patient account (Sheila Tooth) from an NHS AI extra-reader project.