人工智能在骨科领域的全景应用与研究进展

摘要/Abstract

摘要： 系统回顾近年来人工智能（AI）在骨科领域的应用与研究现状,为AI在骨科临床的转化应用与规范化研究提供参考。通过检索PubMed、Web of Science等数据库中近5年AI与骨科交叉领域的相关文献,从影像诊断、手术规划与导航、假体设计、预后预测、智能康复及生成式AI应用等维度进行归纳总结。结果显示近5年AI深度渗透骨科全诊疗流程,在骨折与骨病诊断、精准手术规划与导航、个性化假体研发、手术预后预测及智能康复中取得突破性进展,有效提升了诊疗精准度与效率,但仍存在数据标准不统一、模型可解释性不足、多中心验证缺乏、监管体系不完善等问题。总之,AI为骨科诊疗模式向精准化、个体化、智能化转型提供了核心驱动力,但未来需突破技术与临床转化瓶颈,加强多学科协作与标准化建设,推动AI技术在骨科领域的普惠化、规范化应用。

关键词: 人工智能, 骨科, 深度学习, 手术机器人, 精准医疗

中图分类号:

R68
TP18

刘勃, 马文辉. 人工智能在骨科领域的全景应用与研究进展[J]. 承德医学院学报, 2026, 43(4): 329-335.

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https://tougao.cdmc.edu.cn/CN/Y2026/V43/I4/329

参考文献

[1] Li Y C, Chen H H, Lu H H, et al.Can a deep-learning model for the automated detection of vertebral fractures approach the performance level of human subspecialists?[J]. Clin Orthop Relat Res, 2021, 479(7): 1598-1612.
[2] Sun H B, Wang X, Li Z, et al.Automated rib fracture detection on chest X-ray using contrastive learning[J]. J Digit Imaging, 2023, 36(5): 2138-2147.
[3] Bonakdari H, Jamshidi A, Pelletier J P, et al. A warning machine learning algorithm for early knee osteoarthritis structural progressor patient screening[J]. Ther Adv Musculoskelet Dis, 2021, 23(13): 1759720X21993254.
[4] Liawrungrueang W, Park J B, Cholamjiak W, et al.Artificial Intelligence-Assisted MRI Diagnosis in Lumbar Degenerative Disc Disease: A Systematic Review[J]. Global Spine J, 2025, 15(2): 1405-1418.
[5] Namireddy S R, Gill S S, Peerbhai A, et al.Artificial intelligence in risk prediction and diagnosis of vertebral fractures[J]. Sci Rep, 2024, 14(1): 30560.
[6] Song L, Li C, Tan L, et al.A deep learning model to enhance the classification of primary bone tumors based on incomplete multimodal images in X-ray, CT, and MRI[J]. Cancer Imaging, 2024, 24(1): 135.
[7] Altahtamouni S B, Salman L A, Al-Ani A, et al.The accuracy of artificial intelligence in 3D preoperative planning for total hip arthroplasty: systematic review and meta-analysis[J]. J Exp Orthop, 2026, 13(1): e70427.
[8] Wang M Y, Wen Y, Wang M L, et al.A study on the early clinical outcomes and learning curve of ARTHROBOT-assisted total hip arthroplasty under the ERAS concept[J]. J Robot Surg, 2025, 20(1): 103.
[9] Bunch K M, Greeneway G P, Ansari D S, et al.The symbiosis of robotics, enabling technology and minimally invasive surgery[J]. N Am Spine Soc J, 2025, 23: 100769.
[10] Bhale R, Shahzad H, Price R, et al.The Role of Enabling Technologies in the Surgical Management of Cervical Spine Deformity[J]. Clin Spine Surg, 2025, 38(9): 459-465.
[11] Llombart-Blanco R, Mariscal G, Barrios C, et al.MAKO robot-assisted total hip arthroplasty: a comprehensive meta-analysis of efficacy and safety outcomes[J]. J Orthop Surg Res, 2024, 19: 698.
[12] Hu Y, Fan Y, Sun Z, et al.AI-assisted preoperative planning combined with robotic-assisted total knee arthroplasty vs. conventional surgery: a retrospective controlled study[J]. BMC Musculoskelet Disord, 2026, 27(1): 129.
[13] Sweet M C, Borrelli G J, Manawar S S, et al.Comparison of Outcomes After Robotic-Assisted or Conventional Total Hip Arthroplasty at a Minimum 2-Year Follow-up: A Systematic Review[J]. JBJS Rev, 2021, 9(6).
[14] Kumar V, Patel S, Baburaj V, et al.Does robotic-assisted surgery improve outcomes of total hip arthroplasty compared with manual technique? A systematic review[J]. Postgrad Med J, 2023, 99(1171): 375-383.
[15] Wu B, Wang G, Zheng J, et al.Robot-assisted fracture fixation for pelvic fractures: a scoping review of emerging technologies[J]. Front Surg, 2025, 12: 1559419.
[16] Boudissa M, Khoury G, Franke J, et al.Navigation and 3D-imaging in pelvic ring surgery: a systematic review of prospective comparative studies[J]. Arch Orthop Trauma Surg, 2024, 144(10): 4549-4559.
[17] Ying J W, Cheng L L, Li J L, et al.Treatment of Acetabular Bone Defect in Revision of Total Hip Arthroplasty Using 3D Printed Tantalum Acetabular Augment[J]. Orthop Surg, 2023, 15(5): 1264-1271.
[18] Bruschi A, Donati D M, Bella C D, et al.What to choose in bone tumour resections? Patient specific instrumentation versus surgical navigation: a systematic review[J]. J Bone Oncol, 2023, 42: 100503.
[19] Vucicevic R S, Castonguay J B, Treviño N, et al.Surgeon perspectives on a virtual reality platform for preoperative planning in complex bone sarcomas[J]. J Orthop, 2024, 62: 43-48.
[20] Xu C, Li Z, Kang M, et al.Hydrogel-integrated exosome mimetics derived from osteogenically induced mesenchymal stem cells in spheroid culture enhance bone regeneration[J]. Biomaterials, 2025, 317: 123088.
[21] Chen H, Liu H, Xing L, et al.Deep Learning-driven Microfluidic-SERS to characterize the heterogeneity in exosomes for classifying Non-Small cell lung cancer subtypes[J]. ACS Sens, 2025, 10(4): 2872-2882.
[22] Lu Y, Wang H, Zhang L, et al.Unleashing the power of AI in science-key considerations for materials data preparation[J]. Sci. Data, 2024, 11(1): 1039.
[23] Jang S J, Rosenstadt J, Lee E, et al.Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations[J]. Curr Rev Musculoskelet Med, 2024, 17(6): 185-206.
[24] Wang J, Bi H H, Wang Y W, et al.Application of artificial intelligence in postoperative orthopedic rehabilitation: a scoping review[J]. Front Digit Health, 2026, 7: 1746552.
[25] Lei M, Feng T, Chen M, et al.Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a national cohort study of 52 707 cases[J]. Int J Surg, 2024, 110(8): 4876-4892.
[26] Xing Y, Wang Y, Huang Y, et al.Establishment of a postoperative delirium risk prediction model for elderly hip fracture patients based on machine learning algorithms[J]. BMC Geriatr, 2025, 25: 1033.
[27] Pu H, Shu X, Tan F, et al.Machine learning for predicting extended length of stay in elderly patients with hip fractures: an enhanced recovery after surgery perspective[J]. Digit Health, 2025, 11: 20552076251406311.
[28] Onishchenko D, Rubin D S, van Horne J R, et al. Cardiac Comorbidity Risk Score: Zero-Burden Machine Learning to Improve Prediction of Postoperative Major Adverse Cardiac Events in Hip and Knee Arthroplasty[J]. J Am Heart Assoc, 2022, 11(15): e023745.
[29] Schönnagel L, Caffard T, Vu-Han T L, et al. Predicting postoperative outcomes in lumbar spinal fusion: development of a machine learning model[J]. Spine J, 2024, 24(2): 239-249.
[30] Karabacak M, Bhimani A D, Schupper A J, et al.Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion[J]. BMC Musculoskelet Disord, 2024, 25(1): 401.
[31] Cheng D, Liu D, Li X, et al.A deep learning model for accurately predicting cancer-specific survival in patients with primary bone sarcoma of the extremity: a population-based study[J]. Clin Transl Oncol, 2024, 26(3): 709-719.
[32] Wei S, Wu Z.The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review[J]. Sensors (Basel), 2023, 23(18): 7667.
[33] Tan T, Gatti A A, Fan B, et al.A scoping review of portable sensing for out-of-lab anterior cruciate ligament injury prevention and rehabilitation[J]. NPJ Digit Med, 2023, 6(1): 46.
[34] Billi F, Bini S A.The Application of Agentic Artificial Intelligence in Orthopaedics[J]. J Bone Joint Surg Am, 2026, 108(4): 278-282.
[35] Bouterse A, Pruneski J A, Oettl F C, et al.Artificial intelligence in orthopaedics: Enhanced examinations, ambient intelligence and the future of clinical practice[J]. Knee Surg Sports Traumatol Arthrosc, 2026, 34(5): 1901-1909.
[36] Oettl F C, Pruneski J, Zsidai B, et al.Artificial intelligence agents in orthopaedics: Concepts, capabilities and the road ahead[J]. Knee Surg Sports Traumatol Arthrosc, 2025, 33(12): 4475-4483.
[37] Glinkowski W M, Spalińska A, Wołk A, et al.Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness—A Review[J]. J Clin Med, 2026, 15(5): 1751.
[38] Moons K G M, Damen J A A, Kaul T, et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods[J]. BMJ, 2025, 388: e082505.
[39] CHART Collaborative.Reporting guideline for chatbot health advice studies: Chatbot Assessment Reporting Tool (CHART) statement[J]. BMJ Med, 2025, 4(1): e001632.
[40] Winkler P W, Zsidai B, Senorski E H, et al.A practical guide to the implementation of AI in orthopaedic research-part 7: Risks, limitations, safety and verification of medical AI systems[J]. J Exp Orthop, 2025, 12(2): e70247.
[41] Rouzrokh P, Khosravi B, Faghani S, et al.A Current Review of Generative AI in Medicine: Core Concepts, Applications, and Current Limitations[J]. Curr Rev Musculoskelet Med, 2025, 18(7): 246-266.
[42] Vasey B, Nagendran M, Campbell B, et al.Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI[J]. Nat Med, 2022, 28(5): 924-933.
[43] Loh J Y, Tay X K K, Ng Y H, et al. Big data and artificial intelligence in Orthopaedics: Trends and future directions[J]. J Orthop Surg (Hong Kong), 2026, 34(1): 10225536261424038.