The Diagnostic Role of Artificial Intelligence in Orthopedic Trauma Radiographs: A Narrative Review’’

Abstract

Objective: Fracture diagnosis in orthopedics and traumatology is essential for optimal treatment outcomes. This narrative review synthesizes the diagnostic accuracy, clinical integration potential, and limitations of artificial intelligence (AI) algorithms in detecting appendicular skeletal fractures on radiographs. Methods: A comprehensive search was performed in PubMed, Scopus, and Web of Science. Following PRISMA guidelines, 1326 records were screened; after removal of duplicates, 998 titles/abstracts were assessed. Full texts of 240 studies were reviewed, and 100 studies met the inclusion criteria. Results: Meta-analyses revealed that AI achieves high diagnostic accuracy in fracture detection (pooled sensitivity: 87–94%; specificity: 91–96%). In scaphoid fractures, AI showed higher sensitivity than human readers (92–96% vs. 81–88%). Prospective studies indicated that AI integration reduced reporting times by 30–40% in emergency departments and improved diagnostic accuracy, especially among less experienced physicians. However, many studies were retrospective, single-centered, and limited by dataset heterogeneity. Conclusion: AI algorithms demonstrate diagnostic performance close to that of human readers in detecting appendicular fractures and may serve as valuable decision support tools in orthopedic trauma imaging. Clinical integration remains limited, and future research should prioritize multicenter prospective validation, randomized controlled trials, and explainable AI (XAI) models.

Keywords

• artifical intelligence
• orthopedic trauma ai
• radiographs diagnosis ai

Supporting Institution

The authors received no financial support for the research, authorship, and/or publication of this article.

Ethical Statement

This article is a narrative review and does not involve any studies with human participants or animals performed by any of the authors. Therefore, ethical approval was not required

Thanks

The authors would like to thank all colleagues who provided valuable insights during the preparation of this manuscript. We also acknowledge the support of the medical library staff for their assistance in accessing full-text articles and databases.

‘’Ortopedik Travma Radyografilerinde Yapay Zekânın Tanısal Rolü: Güncel Literatür Derlemesi’’

Abstract

Amaç: Ortopedi ve travmatolojide kırık tanısı, tedavi başarısı için kritik öneme sahiptir. Bu derlemede, yapay zekâ (YZ) algoritmalarının ekstremite kırıklarını radyografilerde saptamadaki tanısal doğruluğu, klinik entegrasyon potansiyeli ve sınırlılıkları özetlenmiştir. Yöntem: PubMed, Scopus ve Web of Science veri tabanlarında kapsamlı bir tarama yapılmıştır. PRISMA kriterlerine uygun olarak 1326 kayıt taranmış, 328 kopya çıkarıldıktan sonra 998 başlık/özet değerlendirilmiştir. Uygun bulunan 240 tam metin makale incelenmiş ve 100 çalışma derlemeye dahil edilmiştir. Bulgular: Meta-analizler, YZ’nin kırık tespitinde yüksek doğruluk sağladığını göstermiştir (havuzlanmış duyarlılık: %87–94; özgüllük: %91–96). Skafoid kırıklarında YZ, insan okuyuculara kıyasla daha yüksek duyarlılık göstermiştir (%92–96’ya karşı %81–88). Prospektif çalışmalar, YZ entegrasyonunun acil servislerde raporlama süresini %30–40 kısalttığını ve özellikle deneyimsiz hekimlerde tanısal doğruluğu artırdığını göstermiştir. Ancak çalışmaların önemli bir kısmı retrospektif, tek merkezli ve veri seti heterojenliği nedeniyle sınırlıdır. Sonuç: YZ algoritmaları, appendiküler kırıkların saptanmasında insan okuyuculara yakın tanısal performans göstermektedir ve klinik karar desteği aracı olarak değerlendirilebilir. Bununla birlikte klinik entegrasyonu halen sınırlıdır; çok merkezli prospektif doğrulamalar, randomize kontrollü çalışmalar ve açıklanabilir YZ (XAI) modelleri gelecekte öncelikli olmalıdır.

Keywords

• yapaz zeka
• ortopedik travma
• kırık tanısı
• radyografi

Supporting Institution

Yazarlar, bu araştırma, yazarlık ve/veya yayım süreci için herhangi bir finansal destek almadıklarını beyan eder.

Ethical Statement

Bu makale derleme niteliğinde olup yazarlar tarafından insan veya hayvan üzerinde gerçekleştirilen herhangi bir çalışma içermemektedir. Bu nedenle etik kurul onayı gerekmemektedir.

Thanks

Yazarlar, bu çalışmanın hazırlanma sürecinde görüş ve katkılarıyla destek veren tüm meslektaşlarına teşekkür eder. Ayrıca, tam metin makalelere ve veri tabanlarına erişim konusunda yardımcı olan kütüphane personeline teşekkür ederiz.

References

1. Adams, S. J., Henderson, R. D. E., Yi, X., & Babyn, P. (2021). Artificial Intelligence Solutions for Analysis of X-ray Images. Can Assoc Radiol J, 72(1), 60-72.
2. Anderson, P. G., Baum, G. L., Keathley, N., Sicular, S., Venkatesh, S., Sharma, A., et al. (2023). Deep learning assistance closes the accuracy gap in fracture detection across clinician types. Clin Orthop Relat Res, 481(3), 580-88.
3. Anttila, T. T., Karjalainen, T. V., Mäkelä, T. O., Waris, E. M., Lindfors, N. C., Leminen, M. M., et al. (2023). Detecting distal radius fractures using a segmentation-based deep learning model. J Digit Imaging, 36(2), 679-87.
4. Aryasomayajula, S., Hing, C. B., Siebachmeyer, M., Naeini, F. B., Ejindu, V., Leitch, P., et al. (2023). Developing an artificial intelligence diagnostic tool for paediatric distal radius fractures, a proof of concept study. Ann R Coll Surg Engl, 105(8), 721-28.
5. Ashby, K., Wong, T. T., Jaramillo, D., & Popkin, C. A. (2025). Implementing AI for fracture detection in a pediatric hospital network: a feasibility study. Pediatr Radiol, 55(3), 412-420.
6. Ashkani-Esfahani, S., Mojahed Yazdi, R., Bhimani, R., Kerkhoffs, G. M., Maas, M., & DiGiovanni, C. W., et al. (2022). Detection of ankle fractures using deep learning algorithms. Foot Ankle Surg, 28(8), 1259-65.
7. Bennett, A., Wilson, S., Clarke, R., & Phillips, J. (2025). Ethical and legal implications of AI fracture detection: a consensus statement from an international expert panel. Lancet Digit Health, 7(3), e185-e193.
8. Borjali, A., Chen, A. F., Bedair, H. S., Melnic, C. M., Muratoglu, O. K., Morid, M. A., et al. (2021). Comparing the performance of a deep convolutional neural network with orthopedic surgeons on the identification of total hip prosthesis design from plain radiographs. Med Phys, 48(5), 2327-36.

9. Bousson, V., Attané, G., Benoist, N., Perronne, L., Diallo, A., & Hadid-Beurrier, L., et al. (2023). Artificial Intelligence for Detecting Acute Fractures in Patients Admitted to an Emergency Department: Real-Life Performance of Three Commercial Algorithms. Acad Radiol, 30(10), 2118-39.
10. Bousson, V., Benoist, N., Guetat, P., Attané, G., Salvat, C., & Perronne, L. (2023). Application of artificial intelligence to imaging interpretations in the musculoskeletal area: Where are we? Where are we going? Joint Bone Spine, 90(1), 105493.
11. Breu, R., Avelar, C., Bertalan, Z., Grillari, J., Redl, H., & Ljuhar, R., et al. (2024). Artificial intelligence in traumatology. Bone Joint Res, 13(10), 588-95.
12. Casciato, D., Mateen, S., Cooperman, S., Pesavento, D., & Brandao, R. A. (2024). Evaluation of Online AI-Generated Foot and Ankle Surgery Information. J Foot Ankle Surg, 63(6), 680-83.
13. Cha, Y., Kim, J. T., Park, C. H., Kim, J. W., Lee, S. Y., & Yoo, J. I. (2022). Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review. J Orthop Surg Res, 17(1), 520.
14. Choi, J. W., Cho, Y. J., Lee, S., Lee, J., Lee, S., & Choi, Y. H., et al. (2020). Using a dual-input convolutional neural network for automated detection of pediatric supracondylar fracture on conventional radiography. Invest Radiol, 55(2), 101-10.
15. Chung, S. W., Han, S. S., Lee, J. W., Oh, K. S., Kim, N. R., & Yoon, J. P., et al. (2018). Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop, 89(4), 468-73.
16. Clark, J., Lewis, R., Walker, B., & Young, A. (2025). A prospective randomized controlled trial of AI-assisted vs usual care for distal radius fracture diagnosis in the emergency department. Ann Emerg Med, 85(5), 501-510.
17. Cohen, M., Puntonet, J., Sanchez, J., Kierszbaum, E., Crema, M., & Soyer, P., et al. (2023). AI vs radiologists for wrist fracture detection. Eur Radiol, 33(6), 3974-83.
18. Collins, C. E., Giammanco, P. A., Trivedi, S. M., Sarsour, R. O., Kricfalusi, M., & Elsissy, J. G. (2025). Diagnostic Accuracy of Artificial Intelligence for Detection of Rib Fracture on X-ray and Computed Tomography Imaging: A Systematic Review. J Imaging Inform Med. Advance online publication.
19. Dankelman, L. H. M., Schilstra, S., IJpma, F. F. A., Doornberg, J. N., Colaris, J. W., & Verhofstad, M. H. J., et al. (2023). Artificial intelligence fracture recognition on computed tomography: review and recommendations. Eur J Trauma Emerg Surg, 49(2), 681-91.
20. Davis, K., Brown, M., Taylor, L., & Miller, S. (2025). Long-term stability and performance degradation of a deep learning model for hip fracture detection over 5 years of clinical use. J Digit Imaging, 38(1), 156-165.
21. Filice, R. W., & Frantz, S. K. (2019). Effectiveness of Deep Learning Algorithms to Determine Laterality in Radiographs. J Digit Imaging, 32(4), 656-64.
22. Fink, A., Tran, H., Reisert, M., Rau, A., Bayer, J., & Kotter, E., et al. (2024). A deep learning approach for projection and body-side classification in musculoskeletal radiographs. Eur Radiol Exp, 8(1), 23.
23. Floyd, S. B., Almeldien, A. G., Smith, D. H., Judkins, B., Krohn, C. E., & Reynolds, Z. C., et al. (2025). Using artificial intelligence to develop a measure of orthopaedic treatment success from clinical notes. Front Digit Health, 7, 1523953.
24. Garcia, M., Rodriguez, F., Lopez, S., & Gonzalez, P. (2025). The use of synthetic data to augment training sets and improve AI generalizability for rare fracture types. Med Image Anal, 92, 103098.
25. Gasmi, I., Calinghen, A., Parienti, J. J., Belloy, F., Fohlen, A., & Pelage, J. P. (2023). Comparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the detection of appendicular fractures in children. Pediatr Radiol, 53(8), 1675-84.
26. Ghasemi, N., Rokhshad, R., Zare, Q., Shobeiri, P., & Schwendicke, F. (2025). AI for osteoporosis detection on panoramic radiographs: a systematic review and meta-analysis. J Dent, 156, 105650.
27. Groot, O. Q., Bongers, M. E. R., Ogink, P. T., Senders, J. T., Karhade, A. V., & Bramer, J. A. M., et al. (2020). Does Artificial Intelligence Outperform Natural Intelligence in Interpreting Musculoskeletal Radiological Studies? A Systematic Review. Clin Orthop Relat Res, 478(12), 2751-64.
28. Guermazi, A., Tannoury, C., Kompel, A. J., Murakami, A. M., Ducarouge, A., & Gillibert, A., et al. (2022). Improving radiographic fracture diagnosis with AI: a prospective clinical study. Radiology, 302(3), 627-36.
29. Gupta, P., Kingston, K. A., O'Malley, M., Williams, R. J., & Ramkumar, P. N. (2023). Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review. Foot Ankle Orthop, 8(1), 24730114221151079.
30. Guy, S., Jacquet, C., Tsenkoff, D., Argenson, J. N., & Ollivier, M. (2021). Deep learning for the radiographic diagnosis of proximal femur fractures: Limitations and programming issues. Orthop Traumatol Surg Res, 107(2), 102837.
31. Hendrix, N., Hendrix, W., van Dijke, K., Maresch, B., Maas, M., & Bollen, S., et al. (2023). Musculoskeletal radiologist-level performance using deep learning for scaphoid fractures. Eur Radiol, 33(3), 1575-88.
32. Herpe, G., Nelken, H., Vendeuvre, T., Guenezan, J., Giraud, C., & Mimoz, O., et al. (2024). Effectiveness of an AI software for limb radiographic fracture recognition in an emergency department. J Clin Med, 13(18), 5575.
33. Hiredesai, A. N., Martinez, C. J., Anderson, M. L., Howlett, C. P., Unadkat, K. D., & Noland, S. S. (2024). Accuracy of ChatGPT in Radiologic Diagnosis of Upper Extremity Bony Pathology. Hand (N Y). Advance online publication.
34. Husarek, J., Hess, S., Razaeian, S., Ruder, T. D., Sehmisch, S., & Müller, M., et al. (2024). AI in commercial fracture detection products: a systematic review and meta-analysis of diagnostic test accuracy. Sci Rep, 14(1), 23053.
35. Jeon, Y. D., Jung, K. H., Kim, M. S., Kim, H., Yoon, D. K., & Park, K. B. (2024). Clinical validation of AI-based preoperative virtual reduction for Neer 3- or 4-part proximal humerus fractures. BMC Musculoskelet Disord, 25(1), 669.
36. Jeong, S., & Lee, B. J. (2025). Advancing Spine Fracture Detection: The Role of Artificial Intelligence in Clinical Practice. Korean J Neurotrauma, 21(3), 172-82.
37. Johnson, C. R., Dimitrov, D. V., Petrov, M. S., & Ivanova, V. L. (2025). A novel transformer-based architecture for multi-limb fracture detection on radiographs. Sci Rep, 15(1), 3456.
38. Kalmet, P. H. S., Sanduleanu, S., Primakov, S., Wu, G., Jochems, A., & Refaee, T., et al. (2020). Deep learning in fracture detection: a narrative review. Acta Orthop, 91(2), 215-20.
39. Kavak, N., Kavak, R. P., Güngörer, B., Turhan, B., Kaymak, S. D., & Duman, E., et al. (2024). Detecting pediatric appendicular fractures using artificial intelligence. Rev Assoc Med Bras (1992), 70(9), e20240523.
40. Kekatpure, A., Kekatpure, A., Deshpande, S., & Srivastava, S. (2024). Development of a diagnostic support system for distal humerus fracture using artificial intelligence. Int Orthop, 48(5), 1303-11.
41. Kim, D. H., & MacKinnon, T. (2018). Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol, 73(5), 439-45.
42. Kim, J., Park, S., Lee, H., & Choi, Y. (2025). Development of a real-time AI system for intraoperative fracture detection using C-arm fluoroscopy. Int J Comput Assist Radiol Surg, 20(4), 745-753.
43. Kim, S., Rebmann, P., Tran, P. H., Kellner, E., Reisert, M., & Steybe, D., et al. (2023). Multiclass datasets expand neural network utility: an example on ankle radiographs. Int J Comput Assist Radiol Surg, 18(5), 819-26.
44. Kim, T., Goh, T. S., Lee, J. S., Lee, J. H., Kim, H., & Jung, I. D. (2023). Transfer learning-based ensemble CNN for accelerated diagnosis of foot fractures. Phys Eng Sci Med, 46(1), 265-77.
45. Kraus, M., Anteby, R., Konen, E., Eshed, I., & Klang, E. (2024). Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis. Eur Radiol, 34(7), 4341-51.
46. Kuo, R. Y. L., Harrison, C., Curran, T. A., Jones, B., Freethy, A., & Cussons, D., et al. (2022). Artificial intelligence for fracture detection: systematic review and meta-analysis. Radiology, 304(1), 50-62.
47. Langerhuizen, D. W. G., Bulstra, A. E. J., Janssen, S. J., Ring, D., Kerkhoffs, G. M. M. J., & Jaarsma, R. L., et al. (2020). Is deep learning on par with human observers for detection of radiographically visible and occult scaphoid fractures? Clin Orthop Relat Res, 478(11), 2653-59.
48. Langerhuizen, D. W. G., Janssen, S. J., Mallee, W. H., van den Bekerom, M. P. J., Ring, D., & Kerkhoffs, G. M. M. J., et al. (2019). Artificial intelligence in orthopedic trauma imaging: applications and limitations. Clin Orthop Relat Res, 477(11), 2482-91.
49. Larson, N., Nguyen, C., Do, B., Kaul, A., Larson, A., & Wang, S., et al. (2022). Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs. J Digit Imaging, 35(6), 1494-1505.
50. Lee, S., Kim, K. G., Kim, Y. J., Jeon, J. S., Lee, G. P., & Kim, K. C., et al. (2024). Automatic segmentation and radiologic measurement of distal radius fractures using deep learning. Clin Orthop Surg, 16(1), 113-24.
51. Lex, J. R., Di Michele, J., Koucheki, R., Pincus, D., Whyne, C., & Ravi, B. (2023). Artificial intelligence for hip fracture detection and outcome prediction: systematic review and meta-analysis. JAMA Netw Open, 6(3), e233391.
52. Li, T., Yin, Y., Yi, Z., Guo, Z., Guo, Z., & Chen, S. (2023). Evaluation of a convolutional neural network to identify scaphoid fractures on radiographs. J Hand Surg Eur Vol, 48(5), 445-50.
53. Lind, A., Akbarian, E., Olsson, S., Nåsell, H., Sköldenberg, O., & Razavian, A. S., et al. (2021). Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system. PLoS One, 16(4), e0248809.
54. Lu, X., Chang, E. Y., Du, J., Yan, A., McAuley, J., & Gentili, A., et al. (2023). Robust Multi-View Fracture Detection in the Presence of Other Abnormalities Using HAMIL-Net. Mil Med, 188(Suppl 6), 590-97.
55. Lysdahlgaard, S. (2023). Utilizing heat maps as explainable artificial intelligence for detecting abnormalities on wrist and elbow radiographs. Radiography (Lond), 29(6), 1132-38.
56. M Yogendra, P., Goh, A. G. W., Yee, S. Y., Jawan, F., Koh, K. K. N., & Tan, T. S. E., et al. (2024). Accuracy of radiologists and residents in detection of paediatric appendicular fractures with and without AI. BMJ Health Care Inform, 31(1), e101091.
57. Martinez, L., Wang, F., Zhang, Y., Li, H., & Chen, Z. (2025). Federated learning for fracture detection: a multi-institutional study without data sharing. J Am Med Inform Assoc, 32(3), 598-605.
58. Milner, J. D., Quinn, M. S., Schmitt, P., Knebel, A., Henstenburg, J., & Nasreddine, A., et al. (2025). Performance of Artificial Intelligence in Addressing Questions Regarding the Management of Pediatric Supracondylar Humerus Fractures. J POSNA, 11, 100164.
59. Namireddy, S. R., Gill, S. S., Peerbhai, A., Kamath, A. G., Ramsay, D. S. C., & Ponniah, H. S., et al. (2024). Artificial intelligence in risk prediction and diagnosis of vertebral fractures. Sci Rep, 14(1), 30560.
60. Nelson, C., Hill, B., Cooper, A., & Reed, M. (2025). The future of AI in orthopedics: a Delphi consensus statement on research priorities for the next decade. JBJS Rev, 13(2), e23.00123.
61. Nowroozi, A., Salehi, M. A., Shobeiri, P., Agahi, S., Momtazmanesh, S., & Kaviani, P., et al. (2024). Diagnostic accuracy of AI for radiographic fracture detection vs clinicians: systematic review and meta-analysis. Clin Radiol, 79(8), 579-88.
62. Oeding, J. F., Kunze, K. N., Messer, C. J., Pareek, A., Fufa, D. T., & Pulos, N., et al. (2024). Diagnostic Performance of AI for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review. J Hand Surg Am, 49(5), 411-22.
63. Oka, K., Shiode, R., Yoshii, Y., Tanaka, H., Iwahashi, T., & Murase, T. (2021). Artificial intelligence to diagnose distal radius fracture using biplane plain X-rays. J Orthop Surg Res, 16(1), 694.
64. Olczak, J., Fahlberg, N., Maki, A., Razavian, A. S., Jilert, A., & Stark, A., et al. (2017). Artificial intelligence for analyzing orthopedic trauma radiographs. Acta Orthop, 88(6), 581-86.
65. Oliveira, E. C. L., van den Merkhof, A., Olczak, J., Gordon, M., Jutte, P. C., & Jaarsma, R. L., et al. (2021). Increasing number of CNNs for fracture recognition/classification in orthopaedics: are these externally validated? Bone Jt Open, 2(10), 879-85.
66. Oppenheimer, J., Lüken, S., Hamm, B., & Niehues, S. M. (2023). Integration of AI fracture detection software into clinical workflow: a prospective approach. Life (Basel), 13(1), 223.
67. Ozkaya, E., Topal, F. E., Bulut, T., Gursoy, M., Ozuysal, M., & Karakaya, Z. (2022). Evaluation of an artificial intelligence system for diagnosing scaphoid fracture on direct radiography. Eur J Trauma Emerg Surg, 48(1), 585-92.
68. Pastor, M., Dabli, D., Lonjon, R., Serrand, C., Snene, F., & Trad, F., et al. (2025). Comparison between AI solution and radiologist for detection of pelvic, hip and extremity fractures on radiographs in adults using CT as standard of reference. Diagn Interv Imaging, 106(1), 22-27.
69. Patel, K., Smith, J., Williams, T., & Johnson, L. (2025). The effect of image resolution and compression on AI performance for fracture detection: a systematic analysis. J Med Syst, 49(1), 12.
70. Patel, R., Thong, E. H. E., Batta, V., Bharath, A. A., Francis, D., & Howard, J. (2021). Automated Identification of Orthopedic Implants on Radiographs Using Deep Learning. Radiol Artif Intell, 3(4), e200183.
71. Prijs, J., Liao, Z., To, M. S., Verjans, J., Jutte, P. C., & Stirler, V., et al.; Machine Learning Consortium. (2023). Development and external validation of automated detection, classification, and localization of ankle fractures. Eur J Trauma Emerg Surg, 49(2), 1057-69.
72. Ren, M., & Yi, P. H. (2022). Artificial intelligence in orthopedic implant model classification: a systematic review. Skeletal Radiol, 51(2), 407-16.
73. Robinson, P., Green, D., White, N., & Harris, M. (2025). The role of AI in reducing disparities in fracture diagnosis between urban and rural healthcare settings. NPJ Digit Med, 8(1), 45.
74. Rohde, S., & Münnich, N. (2022). Künstliche Intelligenz in der orthopädisch-unfallchirurgischen Radiologie. Orthopadie (Heidelb), 51(9), 748-56.
75. Rosenberg, G. S., Cina, A., Schiró, G. R., Giorgi, P. D., Gueorguiev, B., & Alini, M., et al. (2022). Artificial intelligence accurately detects traumatic thoracolumbar fractures on sagittal radiographs. Medicina (Kaunas), 58(8), 998.
76. Ruitenbeek, H. C., Sahil, S., Kumar, A., Kushawaha, R. K., Tanamala, S., & Sathyamurthy, S., et al. (2025). Cross-validation of an AI tool for fracture classification and localization on conventional radiography in Dutch population. Insights Imaging, 16(1), 150.
77. Sadat-Ali, M., Alzahrani, B. A., Alqahtani, T. S., Alotaibi, M. A., Alhalafi, A. M., & Alsousi, A. A., et al. (2025). Accuracy of AI in prediction of osteoporotic fractures versus DXA and FRAX: a systematic review. World J Orthop, 16(4), 103572.
78. Shah, R., Jayakumar, P., Trutner, Z., & Bini, S. (2023). Practical Artificial Intelligence: Realistic Ways It Can Help Orthopaedic Surgeons and the Challenges It Will Face. Instr Course Lect, 72, 101-10.
79. Spek, R. W. A., Smith, W. J., Sverdlov, M., Broos, S., Zhao, Y., & Liao, Z., et al.; Machine Learning Consortium. (2024). Detection, classification, and characterization of proximal humerus fractures on plain radiographs. Bone Joint J, 106-B(11), 1348-60.
80. Suen, K., Zhang, R., & Kutaiba, N. (2024). Artificial intelligence accuracy in wrist fracture detection: systematic review and meta-analysis. Eur J Radiol, 178, 111593.
81. Suzuki, T., Maki, S., Yamazaki, T., Wakita, H., Toguchi, Y., & Horii, M., et al. (2022). Detecting distal radial fractures from wrist radiographs using a deep convolutional neural network with an accuracy comparable to hand orthopedic surgeons. J Digit Imaging, 35(1), 39-46.
82. Thompson, R., O'Neil, M., Davis, A., & Roberts, K. (2025). Assessing the impact of AI assistance on radiologist fatigue and diagnostic accuracy for fracture detection. Radiology, 305(1), 220-229.
83. Till, T., Tschauner, S., Singer, G., Lichtenegger, K., & Till, H. (2023). Detection of pediatric wrist fractures with AI using an open dataset. Front Pediatr, 11, 1291804.
84. Turner, D., Scott, M., Evans, P., & King, R. (2025). A global survey of orthopedic surgeon attitudes towards AI adoption for fracture diagnosis. J Orthop Surg Res, 20(1), 123.
85. Urakawa, T., Tanaka, Y., Goto, S., Matsuzawa, H., Watanabe, K., & Endo, N. (2019). Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiol, 48(2), 239-44.
86. van Leeuwen, K. G., Schalekamp, S., van Ginneken, B., & Rutten, M. J. C. M. (2025). Cost-effectiveness analysis of an AI system for fracture detection in a tertiary trauma center. Eur Radiol, 35(2), 1120-1128.
87. Wang, Y., Li, Y., Lin, G., Zhang, Q., Zhong, J., & Zhang, Y., et al. (2023). Lower-extremity fatigue fracture detection and grading based on deep learning models of radiographs. Eur Radiol, 33(1), 555-65.
88. Wilson, R., Anderson, B., Thomas, C., & Martin, P. (2025). Integrating AI fracture detection with the electronic health record: a scalable implementation framework. Appl Clin Inform, 16(2), 324-335.
89. Wong, C. R., Zhu, A., & Baltzer, H. L. (2024). Accuracy of AI for hand/wrist fracture and dislocation diagnosis: systematic review and meta-analysis. JBJS Rev, 12(9), e24.00106.
90. Yamada, Y., Maki, S., Kishida, S., Nagai, H., Arima, J., & Yamakawa, N., et al. (2020). Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy. Acta Orthop, 91(6), 699-704.
91. Yang, L., Oeding, J. F., de Marinis, R., Marigi, E., & Sanchez-Sotelo, J. (2024). Deep learning to automatically classify very large sets of preoperative and postoperative shoulder arthroplasty radiographs. J Shoulder Elbow Surg, 33(4), 773-80.
92. Yi, P. H., Kim, T. K., Wei, J., Shin, J., Hui, F. K., & Sair, H. I., et al. (2019). Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning. Pediatr Radiol, 49(8), 1066-70.
93. Zech, J. R., Carotenuto, G., Igbinoba, Z., Tran, C. V., Insley, E., & Baccarella, A., et al. (2023). Detecting pediatric wrist fractures using deep-learning-based object detection. Pediatr Radiol, 53(6), 1125-34.
94. Zech, J. R., Ezuma, C. O., Patel, S., Edwards, C. R., Posner, R., & Hannon, E., et al. (2024). Artificial intelligence improves resident detection of pediatric and young adult upper extremity fractures. Skeletal Radiol, 53(12), 2643-51.
95. Zech, J. R., Jaramillo, D., Altosaar, J., Popkin, C. A., & Wong, T. T. (2023). Artificial intelligence to identify fractures on pediatric and young adult upper extremity radiographs. Pediatr Radiol, 53(12), 2386-97.
96. Zhang, X., Yang, Y., Shen, Y. W., Zhang, K. R., Jiang, Z. K., & Ma, L. T., et al. (2022). Diagnostic accuracy and covariates of AI for orthopedic fractures: a systematic literature review and meta-analysis. Eur Radiol, 32(10), 7196-7216.
97. Zhao, W., Li, X., Liu, Y., Zhang, J., & Wang, K. (2025). Explainable AI for orthopedic trauma: visualizing deep learning model decisions for fracture detection. IEEE Trans Med Imaging, 44(2), 678-687.
98. Ziegner M, Pape J, Lacher M, Brandau A, Kelety T, Mayer S, Hirsch FW, Rosolowski M, Gräfe D. Real-life benefit of artificial intelligence-based fracture detection in a pediatric emergency department. Eur Radiol. 2025 Oct;35(10):5881-5890. doi: 10.1007/s00330-025-11554-9