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.
ASJC Scopus subject areas
- Obstetrics and Gynecology