PDF(Pubmed)
Abstract：
The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology, aiming to explore potential directions in future interventional treatments. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure the comprehensiveness and reliability of the results. This review summarizes the latest research directions and developments, ultimately analyzing their corresponding potential and limitations. It furnishes essential information and insights for researchers, clinicians, and policymakers, potentially propelling advancements and innovations within the domains of AI and IR. Finally, our findings indicate that although AI and robotics technologies are not yet widely applied in clinical settings, they are evolving across multiple aspects and are expected to significantly improve the processes and efficacy of interventional treatments.
摘要：
人工智能（AI）和机器人技术的快速发展导致了包括介入放射学（IR）在内的各种医学领域的重大进展。本文综述了人工智能和机器人在红外领域的研究进展和应用，包括深度学习(DL)，机器学习(ML)以及跨越肿瘤学等专业的卷积神经网络(CNN)，神经学,和心脏病学,旨在探讨未来介入治疗的潜在方向。为确保这次审查的广度和深度,我们实施了系统的文献检索策略，选择过去五年内发表的研究。我们在PubMed和GoogleScholar等数据库中进行了搜索，以查找相关文献。特别强调选择大规模研究，以确保结果的全面性和可靠性。本文综述了国内外最新的研究方向和进展,最终分析其相应的潜力和局限性。它为研究人员提供必要的信息和见解，临床医生,和政策制定者，有可能推动AI和IR领域的进步和创新。最后,我们的研究结果表明，尽管人工智能和机器人技术尚未广泛应用于临床环境，它们在多个方面不断发展,有望显著改善介入治疗的流程和疗效.
