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VOLUME 1 , ISSUE 2 ( July-December, 2023 ) > List of Articles


The Transformative Role of Artificial Intelligence in Training Obstetrics and Gynecology Residents

Anuradha Choudhary, Aditya Narayan Choudhary

Keywords : Artificial intelligence, Artificial intelligence in healthcare, Gynecology, Obstetrics, Resident training

Citation Information : Choudhary A, Choudhary AN. The Transformative Role of Artificial Intelligence in Training Obstetrics and Gynecology Residents. J Obstet Gynaecol 2023; 1 (2):61-62.

DOI: 10.5005/jogyp-11012-0012

License: CC BY-NC 4.0

Published Online: 24-11-2023

Copyright Statement:  Copyright © 2023; The Author(s).


  1. Noorbakhsh-Sabet N, Zand R, Zhang Y, et al. Artificial intelligence transforms the future of health care. Am J Med 2019;132(7):795–801. DOI: 10.1016/j.amjmed.2019.01.017.
  2. Knoops PGM, Papaioannou A, Borghi A, et al. A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery. Sci Rep 2019;9:13597. DOI:
  3. Kanevsky J, Corban J, Gaster R, et al. Big data and machine learning in plastic surgery: A new frontier in surgical innovation. Plast Reconstr Surg 2016;137(5):890e–897e. DOI: 10.1097/PRS.0000000000002088.
  4. Mehta N, Devarakonda MV. Machine learning, natural language programming, and electronic health records: The next step in the artificial intelligence journey? J Allergy Clin Immunol 2018;141(6):2019–2021.e1. DOI: 10.1016/j.jaci.2018.02.025.
  5. Pucchio A, Rathagirishnan R, Caton N, et al. Exploration of exposure to artificial intelligence in undergraduate medical education: A Canadian cross-sectional mixed-methods study. BMC Med Educ 2022;22(1):815. DOI: 10.1186/s12909-022-03896-5.
  6. Winkler-Schwartz A, Bissonnette V, Mirchi N, et al. Artificial Intelligence in medical education: Best practices using machine learning to assess surgical expertise in virtual reality simulation. J Surg Educ 2019;76(6):1681–1690. DOI: 10.1016/j.jsurg.2019.05.015.
  7. Kumar Y, Koul A, Singla R, et al. Artificial intelligence in disease diagnosis: A systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput 2023;14(7):8459–8486. DOI: 10.1007/s12652-021-03612-z.
  8. Yi J, Kang HK, Kwon JH, et al. Technology trends and applications of deep learning in ultrasonography: Image quality enhancement, diagnostic support, and improving workflow efficiency. Ultrasonography 2021;40(1):7–22. DOI: 10.14366/usg.20102.
  9. Espinoza J, Good S, Russell E, et al. Does the use of automated fetal biometry improve clinical work flow efficiency? J Ultrasound Med 2013;32(5):847–850. DOI: 10.7863/ultra.32.5.847.
  10. Loftus TJ, Tighe PJ, Filiberto AC, et al. Artificial intelligence and surgical decision-making. JAMA Surg 2020;155(2):148–158. DOI: 10.1001/jamasurg.2019.4917.
  11. Iftikhar P, Kuijpers MV, Khayyat A, et al. Artificial intelligence: A new paradigm in obstetrics and gynecology research and clinical practice. Cureus 2020;12(2):e7124. DOI: 10.7759/cureus.7124.
  12. Anisuzzaman DM, Wang C, Rostami B, et al. Image-based artificial intelligence in wound assessment: A systematic review. Adv Wound Care (New Rochelle) 2022;11(12):687–709. DOI: 10.1089/wound.2021.0091.
  13. Wæhrens EE, Amris K, Fisher AG. Performance-based assessment of activities of daily living (ADL) ability among women with chronic widespread pain: Pain 2010;150(3):535–541. DOI: 10.1016/j.pain.2010.06.008.
  14. Farhud DD, Zokaei S. Ethical issues of artificial intelligence in medicine and healthcare. Iran J Public Health 2021;50(11):i–v. DOI: 10.18502/ijph.v50i11.7600.
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