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Applying Artificial Intelligence to Gynecologic Oncology: A Review.
Mysona, David Pierce; Kapp, Daniel S; Rohatgi, Atharva; Lee, Danny; Mann, Amandeep K; Tran, Paul; Tran, Lynn; She, Jin-Xiong; Chan, John K.
Afiliação
  • Mysona DP; Resident Physician, University of North Carolina, Chapel Hill, NC.
  • Kapp DS; Professor, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA.
  • Rohatgi A; Undergraduate Student, University of California-Davis, Davis, CA.
  • Lee D; Undergraduate Student, University of California-Berkeley, Berkeley, CA.
  • Mann AK; Quantitative Research Analyst, Palo Alto Medical Foundation Research Institute, Palo Alto, CA.
  • Tran P; MD/Phd Student.
  • Tran L; MD/Phd Student.
  • She JX; Professor and Director of the Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta, GA.
  • Chan JK; Director of Gynecologic Oncology, Palo Alto Medical Foundation Research Institute, Palo Alto, CA.
Obstet Gynecol Surv ; 76(5): 292-301, 2021 May.
Article em En | MEDLINE | ID: mdl-34032861
ABSTRACT
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Neoplasias do Colo do Útero Tipo de estudo: Prognostic_studies / Screening_studies Limite: Female / Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Neoplasias do Colo do Útero Tipo de estudo: Prognostic_studies / Screening_studies Limite: Female / Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article