医学
妇科肿瘤学
临床实习
医学物理学
机器学习
人工智能
肿瘤科
计算机科学
家庭医学
作者
Oraianthi Fiste,Michalis Liontos,Flora Zagouri,Georgios S. Stamatakos,Meletios A. Dimopoulos
标识
DOI:10.1016/j.critrevonc.2022.103808
摘要
Machine Learning (ML) represents a computer science capable of generating predictive models, by exposure to raw, training data, without being rigidly programmed. Over the last few years, ML has gained attention within the field of oncology, with considerable strides in both diagnostic, predictive, and prognostic spectrum of malignancies, but also as a catalyst of cancer research. In this review, we discuss the state of ML applications on gynecologic oncology and systematically address major technical and ethical concerns, with respect to their real-world medical practice translation. Undoubtedly, advances in ML will enable the analysis of large, rather complex, datasets for improved, cost-effective, and efficient clinical decisions.
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