从数据到精确：人工智能和机器学习在现代骨科实践中的变革作用

Q2 Medicine

Journal of Clinical Orthopaedics and Trauma Pub Date : 2025-06-18 DOI:10.1016/j.jcot.2025.103101

Amit Kumar Yadav , Prateek Joshi , Anjali Tiwari , Sakshi Watarkar , Ishmita Paul , Gaurav Bhandari

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引用次数: 0

摘要

人工智能（AI）和机器学习（ML）通过将临床问题解决转化为数据驱动的输入输出框架，正在彻底改变骨科手术。人工智能使外科医生和临床医生能够客观地分析问题并提供创新的解决方案。它还使临床医生将这些问题视为一个投入产出连续体，而不是一个需要从基本原则向上解决的障碍。这些技术将允许临床医生绕过对基本原则的传统依赖，而是利用计算模型来优化决策和患者结果。方法使用PubMed、Scopus和IEEE Xplore数据库（2010-2023年）对同行评议的文章进行范围综述，关键词包括人工智能、机器学习、生成式人工智能和临床算法。纳入标准优先考虑了AI/ML在骨科诊断、预测分析或手术计划中的应用。计算能力、深度学习架构和可互操作数据基础设施的进步加速了用于骨科实践的AI/ML工具的发展。关键的创新包括用于术后风险分层的预测算法，用于患者特定植入物设计的生成模型，以及用于术中指导的计算机视觉系统。无处不在的便携式数据采集设备（如平板电脑、语音识别系统）和面向临床医生的软件平台进一步简化了数据聚合，提高了模型的准确性和临床相关性。人工智能/机器学习在骨科手术中的整合是由硬件和软件的协同进步驱动的，为个性化护理、手术精度和结果预测提供了革命性的潜力。未来的采用取决于解决伦理、监管和互操作性方面的挑战，同时促进工程师、临床医生和数据科学家之间的跨学科合作。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

From data to precision: The transformative role of AI and machine learning in modern orthopaedic practice

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From data to precision: The transformative role of AI and machine learning in modern orthopaedic practice

Background

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionising orthopaedic surgery by transforming clinical problem-solving into data-driven input-output frameworks. AI allows surgeons and clinicians to analyse problems and offer innovative solutions objectively. It also enables clinicians to view these problems as an input-output continuum rather than an obstacle that needs to be solved from the basic principles upwards. These technologies would allow clinicians to bypass traditional reliance on foundational principles, instead leveraging computational models to optimise decision-making and patient outcomes.

Methods

A scoping review was conducted using PubMed, Scopus, and IEEE Xplore databases (2010–2023), targeting peer-reviewed articles with keywords including Artificial Intelligence, Machine Learning, Generative AI, and Clinical Algorithms. Inclusion criteria prioritised studies demonstrating AI/ML applications in Orthopaedic diagnostics, predictive analytics, or surgical planning.

Results

Advances in computational power, deep learning architectures, and interoperable data infrastructure have accelerated the development of AI/ML tools for Orthopaedic practice. Key innovations include predictive algorithms for postoperative risk stratification, generative models for patient-specific implant design, and computer vision systems for intraoperative guidance. Ubiquitous adoption of portable data-capture devices (e.g., tablets, voice-recognition systems) and clinician-facing software platforms has further streamlined data aggregation, enhancing model accuracy and clinical relevance.

Conclusion

The integration of AI/ML into Orthopaedic surgery is driven by synergistic advancements in hardware and software, offering transformative potential for personalised care, surgical precision, and outcome prediction. Future adoption hinges on addressing ethical, regulatory, and interoperability challenges while fostering interdisciplinary collaboration between engineers, clinicians, and data scientists.

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来源期刊

Journal of Clinical Orthopaedics and Trauma Medicine-Orthopedics and Sports Medicine

CiteScore

4.30

自引率

0.00%

发文量

181

审稿时长

92 days

期刊介绍： Journal of Clinical Orthopaedics and Trauma (JCOT) aims to provide its readers with the latest clinical and basic research, and informed opinions that shape today''s orthopedic practice, thereby providing an opportunity to practice evidence-based medicine. With contributions from leading clinicians and researchers around the world, we aim to be the premier journal providing an international perspective advancing knowledge of the musculoskeletal system. JCOT publishes content of value to both general orthopedic practitioners and specialists on all aspects of musculoskeletal research, diagnoses, and treatment. We accept following types of articles: • Original articles focusing on current clinical issues. • Review articles with learning value for professionals as well as students. • Research articles providing the latest in basic biological or engineering research on musculoskeletal diseases. • Regular columns by experts discussing issues affecting the field of orthopedics. • "Symposia" devoted to a single topic offering the general reader an overview of a field, but providing the specialist current in-depth information. • Video of any orthopedic surgery which is innovative and adds to present concepts. • Articles emphasizing or demonstrating a new clinical sign in the art of patient examination is also considered for publication. Contributions from anywhere in the world are welcome and considered on their merits.