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本文引用的文献

1
A practical guide to the implementation of AI in orthopaedic research-Part 7: Risks, limitations, safety and verification of medical AI systems.骨科研究中人工智能实施实用指南 - 第7部分:医学人工智能系统的风险、局限性、安全性与验证
J Exp Orthop. 2025 Apr 24;12(2):e70247. doi: 10.1002/jeo2.70247. eCollection 2025 Apr.
2
Exploring artificial intelligence in orthopaedics: A collaborative survey from the ISAKOS Young Professional Task Force.探索骨科领域的人工智能:国际关节镜、膝关节外科与运动医学学会青年专业人员特别工作组的合作调查。
J Exp Orthop. 2025 Feb 24;12(1):e70181. doi: 10.1002/jeo2.70181. eCollection 2025 Jan.
3
AKIRA: Deep learning tool for image standardization, implant detection and arthritis grading to establish a radiographic registry in patients with anterior cruciate ligament injuries.AKIRA:用于图像标准化、植入物检测和关节炎分级的深度学习工具,以建立前交叉韧带损伤患者的放射影像学登记系统。
Knee Surg Sports Traumatol Arthrosc. 2025 Feb 10. doi: 10.1002/ksa.12618.
4
Empowering biomedical discovery with AI agents.利用人工智能代理增强生物医学发现。
Cell. 2024 Oct 31;187(22):6125-6151. doi: 10.1016/j.cell.2024.09.022.
5
Artificial Intelligent Agent Architecture and Clinical Decision-Making in the Healthcare Sector.医疗保健领域的人工智能代理架构与临床决策
Cureus. 2024 Jul 8;16(7):e64115. doi: 10.7759/cureus.64115. eCollection 2024 Jul.
6
Explainable artificial intelligence in orthopedic surgery.骨科手术中的可解释人工智能
J Exp Orthop. 2024 Jul 17;11(3):e12103. doi: 10.1002/jeo2.12103. eCollection 2024 Jul.
7
Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare.公平的人工智能辅助骨科图像分割:解决偏见,促进公平的医疗保健。
Sci Rep. 2024 Jul 12;14(1):16105. doi: 10.1038/s41598-024-66873-6.
8
A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?骨科研究中人工智能实施实用指南,第6部分:如何评估人工智能研究的性能?
J Exp Orthop. 2024 May 31;11(3):e12039. doi: 10.1002/jeo2.12039. eCollection 2024 Jul.
9
Enhanced reliability and time efficiency of deep learning-based posterior tibial slope measurement over manual techniques.与手动技术相比,基于深度学习的胫骨后倾斜率测量具有更高的可靠性和时间效率。
Knee Surg Sports Traumatol Arthrosc. 2025 Jan;33(1):59-69. doi: 10.1002/ksa.12241. Epub 2024 May 26.
10
A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 2: A technical introduction.骨科研究中人工智能实施实用指南——第2部分:技术介绍。
J Exp Orthop. 2024 May 7;11(3):e12025. doi: 10.1002/jeo2.12025. eCollection 2024 Jul.

骨科领域的人工智能主体:概念、能力及未来之路。

Artificial intelligence agents in orthopaedics: Concepts, capabilities and the road ahead.

作者信息

Oettl Felix C, Pruneski James, Zsidai Balint, Yu Yinan, Cong Ting, Feldt Robert, Winkler Philipp W, Hirschmann Michael T, Samuelsson Kristian

机构信息

Department of Orthopedic Surgery, Balgrist University Hospital, University of Zürich, Zurich, Switzerland.

Department of Orthopaedic Surgery, Tripler Army Medical Center, Honolulu, Hawaii, USA.

出版信息

Knee Surg Sports Traumatol Arthrosc. 2025 Dec;33(12):4475-4483. doi: 10.1002/ksa.70109. Epub 2025 Oct 17.

DOI:10.1002/ksa.70109
PMID:41103258
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12684344/
Abstract

Artificial intelligence (AI) is increasingly used in orthopaedics, yet current models are often limited to narrow, isolated tasks like analysing an X-ray or predicting a single outcome. This paper introduces AI agents-a new class of AI systems designed to overcome these limitations. Unlike traditional AI, agents can autonomously manage complex, multistep processes that mirror the complete patient journey. They can coordinate tasks from initial diagnosis and surgical scheduling to postoperative monitoring and rehabilitation, acting as intelligent assistants for clinical teams. This review explains what distinguishes AI agents from conventional AI, explores their potential applications in orthopaedic practice-including perioperative workflow optimisation, research acceleration and intelligent physician support-and discusses the significant implementation and ethical challenges that must be addressed. For the orthopaedic surgeon, understanding AI agents is becoming essential, as these systems offer a transformative potential to enhance efficiency, improve patient outcomes and shape the future of clinical leadership in a technologically advancing field. LEVEL OF EVIDENCE: Level V.

摘要

人工智能(AI)在骨科领域的应用日益广泛,但目前的模型往往局限于狭窄、孤立的任务,如分析X光片或预测单一结果。本文介绍了人工智能智能体——一类旨在克服这些局限性的新型人工智能系统。与传统人工智能不同,智能体能够自主管理复杂的多步骤流程,这些流程反映了完整的患者就医过程。它们可以协调从初始诊断、手术安排到术后监测和康复的各项任务,充当临床团队的智能助手。本综述解释了人工智能智能体与传统人工智能的区别,探讨了它们在骨科实践中的潜在应用——包括围手术期工作流程优化、加速研究以及为医生提供智能支持——并讨论了必须解决的重大实施和伦理挑战。对于骨科外科医生来说,了解人工智能智能体变得至关重要,因为这些系统具有变革潜力,可提高效率、改善患者预后,并在技术不断进步的领域塑造临床领导力的未来。证据级别:V级。