Leszczyńska Agnieszka, Obuchowicz Rafał, Strzelecki Michał, Seweryn Michał
EconMed Europe, Młyńska 9/4, 31-469 Krakow, Poland.
Faculty of Medicine, Andrzej Frycz Modrzewski Krakow University, Gustawa Herlinga-Grudzińskiego 1, 30-705 Krakow, Poland.
J Clin Med. 2025 Sep 1;14(17):6181. doi: 10.3390/jcm14176181.
: This systematic review aims to synthesize recent studies on the integration of artificial intelligence (AI) into robotic surgery for oncological patients. It focuses on studies using real patient data and AI tools in robotic oncologic surgery. : This systematic review followed PRISMA guidelines to ensure a robust methodology. A comprehensive search was conducted in June 2025 across Embase, Medline, Web of Science, medRxiv, Google Scholar, and IEEE databases, using MeSH terms, relevant keywords, and Boolean logic. Eligible studies were original research articles published in English between 2024 and 2025, focusing on AI applications in robotic cancer surgery using real patient data. Studies were excluded if they were non-peer-reviewed, used synthetic/preclinical data, addressed non-oncologic indications, or explored non-robotic AI applications. This approach ensured the selection of studies with practical clinical relevance. : The search identified 989 articles, with 17 duplicates removed. After screening, 921 were excluded, and 37 others were eliminated for reasons such as misalignment with inclusion criteria or lack of full text. Ultimately, 14 articles were included, with 8 using a retrospective design and 6 based on prospective data. These included articles that varied significantly in terms of the number of participants, ranging from several dozen to several thousand. These studies explored the application of AI across various stages of robotic oncologic surgery, including preoperative planning, intraoperative support, and postoperative predictions. The quality of 11 included studies was very good and good. : AI significantly supports robotic oncologic surgery at various stages. In preoperative planning, it helps estimate the risk of conversion from minimally invasive to open colectomy in colon cancer. During surgery, AI enables precise tumor and vascular structure localization, enhancing resection accuracy, preserving healthy tissue, and reducing warm ischemia time. Postoperatively, AI's flexibility in predicting functional and oncological outcomes through context-specific models demonstrates its value in improving patient care. Due to the relatively small number of cases analyzed, further analysis of the issues presented in this review is necessary.
本系统评价旨在综合近期关于将人工智能（AI）整合到肿瘤患者机器人手术中的研究。它聚焦于在机器人肿瘤手术中使用真实患者数据和人工智能工具的研究。本系统评价遵循PRISMA指南以确保方法的稳健性。2025年6月，我们在Embase、Medline、Web of Science、medRxiv、谷歌学术和IEEE数据库中进行了全面检索，使用了医学主题词、相关关键词和布尔逻辑。符合条件的研究是2024年至2025年期间发表的英文原创研究文章，重点是使用真实患者数据的人工智能在机器人癌症手术中的应用。如果研究未经同行评审、使用合成/临床前数据、涉及非肿瘤适应症或探索非机器人人工智能应用，则将其排除。这种方法确保了选择具有实际临床相关性的研究。检索共识别出989篇文章，去除17篇重复文章。经过筛选，排除了921篇，另有37篇因不符合纳入标准或缺乏全文等原因被剔除。最终纳入14篇文章，其中8篇采用回顾性设计，6篇基于前瞻性数据。这些文章的参与者数量差异很大，从几十到几千不等。这些研究探讨了人工智能在机器人肿瘤手术各个阶段的应用，包括术前规划、术中支持和术后预测。11篇纳入研究的质量非常好或良好。人工智能在各个阶段都显著支持机器人肿瘤手术。在术前规划中，它有助于估计结肠癌从微创结肠切除术转为开放结肠切除术的风险。在手术过程中，人工智能能够精确地定位肿瘤和血管结构，提高切除精度，保留健康组织，并减少热缺血时间。术后，人工智能通过特定情境模型预测功能和肿瘤学结果的灵活性证明了其在改善患者护理方面的价值。由于分析的病例数量相对较少，有必要对本综述中提出的问题进行进一步分析。
