Introduction: Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. Methodology: We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. Results: Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. Conclusions: AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.
引言：人工智能正通过推动医学各领域的创新、自动化与优化，深刻变革着医疗健康行业。本研究旨在探讨人工智能技术是否可应用于泌尿系统肿瘤的诊断、治疗规划及监测。方法：我们系统检索了截至2022年5月31日收录于PUBMED/Scopus数据库的原创性与综述性文献，检索策略涵盖人工智能与泌尿肿瘤学的相关术语，文献筛选经由全体作者共识确认。结果：多种人工智能技术可应用于医疗领域，其中最常见的形式包括机器学习、深度学习、神经网络、自然语言处理系统及计算机视觉。人工智能能够提升泌尿系统肿瘤管理的多个维度，如影像分析、病理分级及淋巴结分期评估，同时有助于精准诊断、治疗方案选择乃至生物标志物识别。在多数应用场景中，人工智能的诊断准确度已达到甚至超越临床医生水平。结论：人工智能技术有望革新泌尿系统肿瘤的诊断、治疗与监测模式。未来人工智能在泌尿肿瘤诊疗中的应用将日益广泛，从而提升患者预后并优化肿瘤整体管理水平。
Artificial Intelligence in Urooncology: What We Have and What We Expect
肿瘤(癌症)患者之家