ARTIFICIAL INTELLIGENCE IN RADIOGRAPHIC FRACTURE DIAGNOSIS: CURRENT APPLICATIONS, CLINICAL UTILITY, AND LIMITATIONS: A NARRATIVE REVIEW

Mateusz Onopiuk; Jowita Wiktoria Maksymiuk; Urszula Gadomska; Natalia Dejewska; Zuzanna Walewska; Zuzanna Wiktoria Szumska; Mikołaj Daniluk; Jonasz Żuk; Kinga Bukała; Szymon Klimaszewski

doi:10.31435/ijitss.2(50).2026.6148

Authors

Mateusz Onopiuk Bielański Hospital named after Rev. Jerzy Popiełuszko, Warsaw, Poland https://orcid.org/0009-0003-9916-1292
Jowita Wiktoria Maksymiuk Bielański Hospital named after Rev. Jerzy Popiełuszko, Warsaw, Poland https://orcid.org/0009-0004-3130-9913
Urszula Gadomska Bielański Hospital named after Rev. Jerzy Popiełuszko, Warsaw, Poland https://orcid.org/0009-0005-8381-9955
Natalia Dejewska Bielański Hospital named after Rev. Jerzy Popiełuszko, Warsaw, Poland https://orcid.org/0009-0006-4806-6780
Zuzanna Walewska Bielański Hospital named after Rev. Jerzy Popiełuszko, Warsaw, Poland https://orcid.org/0009-0001-0088-4294
Zuzanna Wiktoria Szumska Szpital Powiatowy w Otwocku, Otwock, Poland https://orcid.org/0009-0009-5287-9946
Mikołaj Daniluk Centralny Szpital Kliniczny, Poland https://orcid.org/0009-0005-6458-373X
Jonasz Żuk Centralny Szpital Kliniczny, Poland https://orcid.org/0009-0001-7500-7982
Kinga Bukała Medical University of Lodz, Lodz, Poland https://orcid.org/0009-0003-9587-4826
Szymon Klimaszewski Praktyka Lekarska Dentystyczna, Poland https://orcid.org/0009-0001-3505-2797

DOI:

https://doi.org/10.31435/ijitss.2(50).2026.6148

Keywords:

Artificial intelligence, Deep Learning, Fracture Detection, Musculoskeletal Imaging, Emergency Radiology, Clinical Decision Support, Missed Fractures

Abstract

Artificial intelligence (AI) is increasingly investigated as a supportive tool in radiographic fracture diagnosis, particularly in emergency and musculoskeletal imaging. This narrative review aims to summarize current evidence on the use of AI in fracture detection, clinician-assisted interpretation, fracture classification, workflow optimization, and implementation challenges. A literature search was conducted in PubMed using combinations of terms related to artificial intelligence, deep learning, convolutional neural networks, fracture detection, radiography, emergency radiology, and clinical decision support. Priority was given to systematic reviews, meta-analyses, diagnostic accuracy studies, reader studies, and real-world clinical evaluations. Current evidence indicates that AI-based systems can achieve high diagnostic performance in selected fracture detection tasks, especially when applied to anatomically focused radiographic datasets. Studies assessing AI-assisted interpretation suggest that these tools may improve clinicians’ sensitivity, specificity, and overall diagnostic performance, with the greatest benefit observed among less experienced readers and non-radiologist clinicians. AI may also support fracture classification and workflow optimization, although these applications remain less extensively validated. Important limitations include restricted generalizability, reduced performance in subtle or complex fractures, limited external validation, and a shortage of prospective real-world studies. Overall, AI represents a promising but still evolving adjunct in radiographic fracture diagnosis and should be developed as a decision-support tool integrated with clinical expertise.

References

Al Hajaj, S. W., Soliman, K., Zafar, M., Garnham, C., Al Hajaj, D., Elshafie, O., Alsswah, A., & Elwan, M. H. (2026). From subtle breaks to missed diagnoses: Real-world evaluation of an artificial intelligence fracture detection tool. World Journal of Orthopedics, 17(4). https://doi.org/10.5312/wjo.v17.i4.113710

Anderson, P. G., Baum, G. L., Keathley, N., Sicular, S., Venkatesh, S., Sharma, A., Daluiski, A., Potter, H., Hotchkiss, R., Lindsey, R. V., & Jones, R. M. (2023). Deep Learning Assistance Closes the Accuracy Gap in Fracture Detection Across Clinician Types. Clinical Orthopaedics & Related Research, 481(3), 580–588. https://doi.org/10.1097/CORR.0000000000002385

Avanzo, M., Stancanello, J., Pirrone, G., Drigo, A., & Retico, A. (2024). The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning. Cancers, 16(21), 3702. https://doi.org/10.3390/cancers16213702

Bachmann, R., Gunes, G., Hangaard, S., Nexmann, A., Lisouski, P., Boesen, M., Lundemann, M., & Baginski, S. G. (2023). Improving traumatic fracture detection on radiographs with artificial intelligence support: A multi-reader study. BJR|Open, 6(1), tzae011. https://doi.org/10.1093/bjro/tzae011

Chen, H.-Y., Hsu, B. W.-Y., Yin, Y.-K., Lin, F.-H., Yang, T.-H., Yang, R.-S., Lee, C.-K., & Tseng, V. S. (2021). Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs. PLOS ONE, 16(1), e0245992. https://doi.org/10.1371/journal.pone.0245992

Cheng, C.-T., Wang, Y., Chen, H.-W., Hsiao, P.-M., Yeh, C.-N., Hsieh, C.-H., Miao, S., Xiao, J., Liao, C.-H., & Lu, L. (2021). A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs. Nature Communications, 12(1), 1066. https://doi.org/10.1038/s41467-021-21311-3

Chung, S. W., Han, S. S., Lee, J. W., Oh, K.-S., Kim, N. R., Yoon, J. P., Kim, J. Y., Moon, S. H., Kwon, J., Lee, H.-J., Noh, Y.-M., & Kim, Y. (2018). Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthopaedica, 89(4), 468–473. https://doi.org/10.1080/17453674.2018.1453714

Currie, G., Hawk, K. E., Rohren, E., Vial, A., & Klein, R. (2019). Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. Journal of Medical Imaging and Radiation Sciences, 50(4), 477–487. https://doi.org/10.1016/j.jmir.2019.09.005

Duron, L., Ducarouge, A., Gillibert, A., Lainé, J., Allouche, C., Cherel, N., Zhang, Z., Nitche, N., Lacave, E., Pourchot, A., Felter, A., Lassalle, L., Regnard, N.-E., & Feydy, A. (2021). Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study. Radiology, 300(1), 120–129. https://doi.org/10.1148/radiol.2021203886

Elbahi, M. K., Muhammed, A., Fadlelmola Abdalla Mohamednour, M., & Mukhtar, F. S. (2025). Artificial Intelligence in Fracture Diagnosis on Radiographs: Evidence, Pitfalls, and Pathways for Clinical Integration (2020–2025). Cureus. https://doi.org/10.7759/cureus.93124

Fernholm, R., Pukk Härenstam, K., Wachtler, C., Nilsson, G. H., Holzmann, M. J., & Carlsson, A. C. (2019). Diagnostic errors reported in primary healthcare and emergency departments: A retrospective and descriptive cohort study of 4830 reported cases of preventable harm in Sweden. European Journal of General Practice, 25(3), 128–135. https://doi.org/10.1080/13814788.2019.1625886

Guermazi, A., Tannoury, C., Kompel, A. J., Murakami, A. M., Ducarouge, A., Gillibert, A., Li, X., Tournier, A., Lahoud, Y., Jarraya, M., Lacave, E., Rahimi, H., Pourchot, A., Parisien, R. L., Merritt, A. C., Comeau, D., Regnard, N.-E., & Hayashi, D. (2022). Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence. Radiology, 302(3), 627–636. https://doi.org/10.1148/radiol.210937

Herpe, G., Nelken, H., Vendeuvre, T., Guenezan, J., Giraud, C., Mimoz, O., Feydy, A., Tasu, J.-P., & Guillevin, R. (2024). Effectiveness of an Artificial Intelligence Software for Limb Radiographic Fracture Recognition in an Emergency Department. Journal of Clinical Medicine, 13(18), 5575. https://doi.org/10.3390/jcm13185575

Huhtanen, J. T., Nyman, M., Blanco Sequeiros, R., Koskinen, S. K., Pudas, T. K., Kajander, S., Niemi, P., Aronen, H. J., & Hirvonen, J. (2025). Comparative accuracy of two commercial AI algorithms for musculoskeletal trauma detection in emergency radiographs. Emergency Radiology, 32(4), 569–580. https://doi.org/10.1007/s10140-025-02353-2

Jung, J., Dai, J., Liu, B., & Wu, Q. (2024). Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis. PLOS Digital Health, 3(1), e0000438. https://doi.org/10.1371/journal.pdig.0000438

Krogue, J. D., Cheng, K. V., Hwang, K. M., Toogood, P., Meinberg, E. G., Geiger, E. J., Zaid, M., McGill, K. C., Patel, R., Sohn, J. H., Wright, A., Darger, B. F., Padrez, K. A., Ozhinsky, E., Majumdar, S., & Pedoia, V. (2020). Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning. Radiology: Artificial Intelligence, 2(2), e190023. https://doi.org/10.1148/ryai.2020190023

Kuo, R. Y. L., Harrison, C., Curran, T.-A., Jones, B., Freethy, A., Cussons, D., Stewart, M., Collins, G. S., & Furniss, D. (2022). Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis. Radiology, 304(1), 50–62. https://doi.org/10.1148/radiol.211785

Lindsey, R., Daluiski, A., Chopra, S., Lachapelle, A., Mozer, M., Sicular, S., Hanel, D., Gardner, M., Gupta, A., Hotchkiss, R., & Potter, H. (2018). Deep neural network improves fracture detection by clinicians. Proceedings of the National Academy of Sciences, 115(45), 11591–11596. https://doi.org/10.1073/pnas.1806905115

Liu, P., Lu, L., Zhang, J., Huo, T., Liu, S., & Ye, Z. (2021). Application of Artificial Intelligence in Medicine: An Overview. Current Medical Science, 41(6), 1105–1115. https://doi.org/10.1007/s11596-021-2474-3

Luiken, I., Lemke, T., Komenda, A., Marka, A. W., Kim, S. H., Graf, M. M., Ziegelmayer, S., Weller, D., Mertens, C. J., Bressem, K. K., Makowski, M. R., Adams, L. C., Prucker, P., & Busch, F. (2025). Evaluation of commercial AI algorithms for the detection of fractures, effusions, and dislocations on real-world clinical data: A prospective registry study. Radiography, 31(6), 103189. https://doi.org/10.1016/j.radi.2025.103189

Newman-Toker, D. E., Peterson, S. M., Badihian, S., Hassoon, A., Nassery, N., Parizadeh, D., Wilson, L. M., Jia, Y., Omron, R., Tharmarajah, S., Guerin, L., Bastani, P. B., Fracica, E. A., Kotwal, S., & Robinson, K. A. (2022). Diagnostic Errors in the Emergency Department: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ). https://doi.org/10.23970/AHRQEPCCER258

Pinto, A., Berritto, D., Russo, A., Riccitiello, F., Caruso, M., Belfiore, M. P., Papapietro, V. R., Carotti, M., Pinto, F., Giovagnoni, A., Romano, L., & Grassi, R. (2018). Traumatic fractures in adults: Missed diagnosis on plain radiographs in the Emergency Department. Acta Biomedica Atenei Parmensis, 89(1-S), 111–123. https://doi.org/10.23750/abm.v89i1-S.7015

Pinto, A., Reginelli, A., Pinto, F., Lo Re, G., Midiri, F., Muzj, C., Romano, L., & Brunese, L. (2016). Errors in imaging patients in the emergency setting. The British Journal of Radiology, 89(1061), 20150914. https://doi.org/10.1259/bjr.20150914

Qin, H., Ding, Y., Ju, J., Qu, Z., & Peng, L. (2026). Enhanced fracture detection on radiographs with AI assistance for clinicians: A systematic review and meta-analysis. Annals of Medicine, 58(1), 2610079. https://doi.org/10.1080/07853890.2025.2610079

Somville, F., Van Bogaert, P., Wellens, B., De Cauwer, H., & Franck, E. (2024). Work stress and burnout among emergency physicians: A systematic review of last 10 years of research. Acta Clinica Belgica, 79(1), 52–61. https://doi.org/10.1080/17843286.2023.2273611

Wan, Z., Tang, J., Bai, X., Cao, Y., Zhang, D., Su, T., Zhou, Y., Qiao, L., Shen, K., Wang, L., Tian, X., & Wang, J. (2023). Burnout among radiology residents: A systematic review and meta-analysis. European Radiology, 34(2), 1399–1407. https://doi.org/10.1007/s00330-023-09986-2

Zha, N., Patlas, M. N., & Duszak, R. (2019). Radiologist Burnout Is Not Just Isolated to the United States: Perspectives From Canada. Journal of the American College of Radiology, 16(1), 121–123. https://doi.org/10.1016/j.jacr.2018.07.010