Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vasilissis Sophias, 11528 Athens, Greece.
Department of Clinical Therapeutics, School of Medicine, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vasilissis Sophias, 11528 Athens, Greece.
Crit Rev Oncol Hematol. 2022 Nov;179:103808. doi: 10.1016/j.critrevonc.2022.103808. Epub 2022 Sep 7.
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.
机器学习（ML）是一门计算机科学，能够通过对原始训练数据进行处理来生成预测模型，而无需进行严格的编程。在过去的几年中，ML 在肿瘤学领域引起了关注，在诊断、预测和恶性肿瘤预后方面都取得了相当大的进展，但它也是癌症研究的催化剂。在这篇综述中，我们讨论了 ML 在妇科肿瘤学中的应用现状，并系统地讨论了与将其实际应用于医学实践相关的主要技术和伦理问题。毫无疑问，ML 的进步将能够分析更大、更复杂的数据集，以做出更好、更具成本效益和更有效的临床决策。
