Articles

Artificial Intelligence in Healthcare

AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone

Academic Edition, Chapter 28 - References/Further Reading

General Edition, Chapter 11 - References/Further Reading

 

  1. Mehrizi MH, van Ooijen P, Homan M. Applications of artificial intelligence (AI) in diagnostic radiology: a technography study. European Radiology. 2020 Sep 18:1-7.
  2. How radiologists are using machine learning [Internet]. Diagnostic Imaging. [cited 2021 Jan 21]. Available from: https://www.diagnosticimaging.com/view/how-radiologists-are-using-machine-learning
  3. Medical imaging cloud ai [Internet]. Arterys. [cited 2021 Jan 21]. Available from: https://arterys.com/
  4. Arterys cardio dl cloud mri analytics software receives fda clearance [Internet]. DAIC. 2017 [cited 2021 Jan 21]. Available from: https://www.dicardiology.com/product/arterys-cardio-dl-cloud-mri-analytics-software-receives-fda-clearance
  5. Arterys partners with ge healthcare to launch viosworks cardiac imaging platform [Internet]. DAIC. 2015 [cited 2021 Jan 21]. Available from: https://www.dicardiology.com/product/arterys-partners-ge-healthcare-launch-viosworks-cardiac-imaging-platform
  6. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv preprint arXiv:2003.05037. 2020 Mar 10.
  7. Home [Internet]. RADLogics. [cited 2021 Jan 21]. Available from: https://www.radlogics.com/
  8. Ibm watson health | ai healthcare solutions [Internet]. IBM Watson Health. [cited 2021 Jan 21]. Available from: https://www.ibm.com/watson-health
  9. Artificial intelligence [Internet]. CAR - Canadian Association of Radiologists. [cited 2021 Jan 21]. Available from: https://car.ca/innovation/artificial-intelligence/
  10. Wolf M, Krause J, Carney PA, Bogart A, Kurvers RH. Collective intelligence meets medical decision-making: the collective outperforms the best radiologist. PloS one. 2015 Aug 12;10(8):e0134269.
  11. Mass General, Brigham and Women’s to apply deep learning to medical records and images [Internet]. Healthcare IT News. 2018 [cited 2021 Jan 21]. Available from: https://www.healthcareitnews.com/news/mass-general-brigham-and-womens-apply-deep-learning-medical-records-and-images
  12. Newsroom N. Nvidia launches world’s first deep learning supercomputer [Internet]. NVIDIA Newsroom Newsroom. [cited 2021 Jan 21]. Available from: http://nvidianews.nvidia.com/news/nvidia-launches-world-s-first-deep-learning-supercomputer
  13. What artificial intelligence can do for radiology [Internet]. Diagnostic Imaging. [cited 2021 Jan 21]. Available from: https://www.diagnosticimaging.com/view/what-artificial-intelligence-can-do-radiology
  14. Predible health: using tech to diagnose diseases [Internet]. Forbes India. [cited 2021 Jan 21]. Available from: https://www.forbesindia.com/article/30-under-30-2020/predible-health-using-tech-to-diagnose-diseases/57703/1
  15. Taslakian B, Ingber R, Aaltonen E, Horn J, Hickey R. Interventional Radiology Suite: A Primer for Trainees. Journal of Clinical Medicine 2019;8:1347. https://doi.org/10.3390/jcm8091347.
  16. Iezzi R, Goldberg SN, Merlino B, Posa A, Valentini V, Manfredi R. Artificial Intelligence in Interventional Radiology: A Literature Review and Future Perspectives. Journal of Oncology 2019;2019:e6153041. https://doi.org/10.1155/2019/6153041.
  17. Meek RD, Lungren MP, Gichoya JW. Machine Learning for the Interventional Radiologist. American Journal of Roentgenology 2019;213:782–4. https://doi.org/10.2214/AJR.19.21527.
  18. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging 2018;9:611–29. https://doi.org/10.1007/s13244-018-0639-9.
  19. Corindus CorPath GRX for PCI and PVI Procedures [Internet]. Corindus.com. [cited 2021 Jul 28]. Available from: https://www.corindus.com/corpath-grx/how-it-works
  20. Campbell PT, Kruse KR, Kroll CR, Patterson JY, Esposito MJ. The impact of precise robotic lesion length measurement on stent length selection: ramifications for stent savings. Cardiovasc Revasc Med. 2015;16(6):348–50.
  21. Weisz G, Metzger DC, Caputo RP, Delgado JA, Marshall JJ, Vetrovec GW, et al. Safety and feasibility of robotic percutaneous coronary intervention: PRECISE (Percutaneous Robotically-Enhanced Coronary Intervention) Study. J Am Coll Cardiol. 2013;61(15):1596–600.
  22. Gurgitano M, Angileri SA, Rodà GM, Liguori A, Pandolfi M, Ierardi AM, et al. Interventional Radiology ex-machina: impact of Artificial Intelligence on practice. Radiol Med 2021;126:998–1006. https://doi.org/10.1007/s11547-021-01351-x.