TY - JOUR
T1 - Applying artificial intelligence to gynecologic oncology
T2 - A review
AU - Mysona, David Pierce
AU - Kapp, Daniel S.
AU - Rohatgi, Atharva
AU - Lee, Danny
AU - Mann, Amandeep K.
AU - Tran, Paul
AU - Tran, Lynn
AU - She, Jin Xiong
AU - Chan, John K.
N1 - Funding Information:
Dr Chan discloses that he is a recipient of grant/research funding from Acerta, Aravive, Biodesix, Clovis, Johnson & Johnson, Oxigen, Genentech, Tesaro, AstraZeneca, Eisai, and Merck. This project was supported by Denise Hale Chair and Fisher Family Fund from Dr John Chan. The remaining authors, faculty, and staff in a position to control the content of this CME activity have disclosed that they have no financial relationships with, or financial interests in, any commercial organizations relevant to this educational activity.
Publisher Copyright:
© 2021 Lippincott Williams and Wilkins. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Importance: Artificial intelligence (AI) will play an increasing role in health care. In gynecologic oncology, it can advance tailored screening, precision surgery, and personalized targeted therapies. Objective: The aim of this study was to review the role of AI in gynecologic oncology. Evidence Acquisition: Artificial intelligence publications in gynecologic oncology were identified by searching “gynecologic oncology AND artificial intelligence” in the PubMed database. A review of the literature was performed on the history of AI, its fundamentals, and current applications as related to diagnosis and treatment of cervical, uterine, and ovarian cancers. Results: A PubMed literature search since the year 2000 showed a significant increase in oncology publications related to AI and oncology. Early studies focused on using AI to interrogate electronic health records in order to improve clinical outcome and facilitate clinical research. In cervical cancer, AI algorithms can enhance image analysis of cytology and visual inspection with acetic acid or colposcopy. In uterine cancers, AI can improve the diagnostic accuracies of radiologic imaging and predictive/prognostic capabilities of clinicopathologic characteristics. Artificial intelligence has also been used to better detect early-stage ovarian cancer and predict surgical outcomes and treatment response. Conclusions and Relevance: Artificial intelligence has been shown to enhance diagnosis, refine clinical decision making, and advance personalized therapies in gynecologic cancers. The rapid adoption of AI in gynecologic oncology will depend on overcoming the challenges related to data transparency, quality, and interpretation. Artificial intelligence is rapidly transforming health care. However, many physicians are unaware that this technology is being used in their practices and could benefit from a better understanding of the statistics and computer science behind these algorithms. This review provides a summary of AI, its applicability, and its limitations in gynecologic oncology. Target Audience: Obstetricians and gynecologists, family physicians Learning Objectives: After completing this CME activity, physicians should be better able to describe the basic functions of AI algorithms; explain the potential applications of machine learning in diagnosis, treatment, and prognostication of cervical, endometrial, and ovarian cancers; and identify the ethical concerns and limitations of the use of AI in the management of gynecologic cancer patients.
AB - Importance: Artificial intelligence (AI) will play an increasing role in health care. In gynecologic oncology, it can advance tailored screening, precision surgery, and personalized targeted therapies. Objective: The aim of this study was to review the role of AI in gynecologic oncology. Evidence Acquisition: Artificial intelligence publications in gynecologic oncology were identified by searching “gynecologic oncology AND artificial intelligence” in the PubMed database. A review of the literature was performed on the history of AI, its fundamentals, and current applications as related to diagnosis and treatment of cervical, uterine, and ovarian cancers. Results: A PubMed literature search since the year 2000 showed a significant increase in oncology publications related to AI and oncology. Early studies focused on using AI to interrogate electronic health records in order to improve clinical outcome and facilitate clinical research. In cervical cancer, AI algorithms can enhance image analysis of cytology and visual inspection with acetic acid or colposcopy. In uterine cancers, AI can improve the diagnostic accuracies of radiologic imaging and predictive/prognostic capabilities of clinicopathologic characteristics. Artificial intelligence has also been used to better detect early-stage ovarian cancer and predict surgical outcomes and treatment response. Conclusions and Relevance: Artificial intelligence has been shown to enhance diagnosis, refine clinical decision making, and advance personalized therapies in gynecologic cancers. The rapid adoption of AI in gynecologic oncology will depend on overcoming the challenges related to data transparency, quality, and interpretation. Artificial intelligence is rapidly transforming health care. However, many physicians are unaware that this technology is being used in their practices and could benefit from a better understanding of the statistics and computer science behind these algorithms. This review provides a summary of AI, its applicability, and its limitations in gynecologic oncology. Target Audience: Obstetricians and gynecologists, family physicians Learning Objectives: After completing this CME activity, physicians should be better able to describe the basic functions of AI algorithms; explain the potential applications of machine learning in diagnosis, treatment, and prognostication of cervical, endometrial, and ovarian cancers; and identify the ethical concerns and limitations of the use of AI in the management of gynecologic cancer patients.
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U2 - 10.1097/ogx.0000000000000902
DO - 10.1097/ogx.0000000000000902
M3 - Review article
C2 - 34032861
AN - SCOPUS:85106879944
SN - 0029-7828
VL - 76
SP - 292
EP - 301
JO - Obstetrical and Gynecological Survey
JF - Obstetrical and Gynecological Survey
IS - 5
ER -