ARTIFICIAL INTELLIGENCE IN MUSCULOSKELETAL ULTRASOUND FOR SPORTS INJURIES: DIAGNOSTIC ACCURACY, CLINICAL UTILITY, AND IMPLEMENTATION CHALLENGES (2022-2026): A NARRATIVE REVIEW
DOI:
https://doi.org/10.31435/ijitss.3(51).2026.5900Keywords:
Artificial Intelligence, Musculoskeletal Ultrasound, Sports Injuries, Point-of-care Ultrasound, Sports Medicine, Tendon Injury, Muscle Injury, Diagnostic Imaging.Abstract
Background: Musculoskeletal Ultrasound (MSK-US) is a popular tool among Sports Medicine professionals due to its portability, repeatability, no radiation exposure, and ability to provide dynamic, point-of-care assessments. However, MSK-US also has an operator dependent component of accuracy.
Objective: This Review will summarize the current literature from 2022-2026 regarding Artificial Intelligence (AI) in MSK-US specifically with respect to its use in assessing sports injuries, including its diagnostic accuracy, utility and readiness.
Methods: The search engines PubMed, Google Scholar, and Reference Chaining were utilized to identify peer-reviewed publications that contained information related to AI; MSK Ultrasound; Sports Medicine; Tendons; Muscle Injuries; Rotator Cuff Injuries; Point-of-Care Imaging.
Results: Potential uses of AI in MSK-US are based on pattern recognition, automated measurements, segmentation, and as an aid to second readers, particularly when assessing injuries to tendons, muscles, or the rotator cuff. AI can potentially help reduce variability and enable consistent monitoring. The quality of the available evidence was limited by studies using retrospectively collected data; curated databases; heterogeneity of study design; lack of external validity; and limited examination of outcomes relevant to patients.
Conclusion: AI-assisted MSK US appears to be a promising technology; however, at present it is not ready for unqualified clinical application. The most likely near term use of AI-assisted MSK US would be as a form of supervised decision support for trained health care providers.
References
Allen, G. M. (2021). Ultrasonography: Sports injuries. In StatPearls. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK570156/
Bolek, M., Musialska, D., Kędzia, A., Jagieła, B., Fidyk, M., Minkiewicz, M., & Dyda, M. (2025). The application of artificial intelligence in medical diagnostics: Implications for sports medicine. Quality in Sport, 34, 60392. https://doi.org/10.12775/QS.2025.34.60392
Cushman, D. M., Stokes, D., Vu, L., Corcoran, B., Fredericson, M., Eby, S. F., & Teramoto, M. (2025). Ultrasound as a predictor of time-loss injury for the patellar tendon, Achilles tendon and plantar fascia in division I collegiate athletes. British Journal of Sports Medicine, 59(4), bjsports-2024-109066. https://doi.org/10.1136/bjsports-2024-109066
Getzmann, J. M., Albano, D., Serpi, F., et al. (2024). The use of artificial intelligence in musculoskeletal ultrasound: A systematic review of the literature. La Radiologia Medica, 129(9), 1405-1411. https://doi.org/10.1007/s11547-024-01856-1
Ghosh, S., Felfeliyan, B., Zhou, Y., Knight, J., Akhlaq, N., Küpper, J., Hareendranathan, A. R., & Jaremko, J. L. (2024). Ultrasound for automated classification of full-thickness rotator cuff tendon tears using deep learning. In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1-4. https://doi.org/10.1109/EMBC53108.2024.10782185
Koziak, W., et al. (2025). The application of point-of-care ultrasound in sports medicine: A narrative review. Quality in Sport, 39, 58497. https://doi.org/10.12775/QS.2025.39.58497
Manske, R. C., et al. (2025). Diagnostic musculoskeletal ultrasound in the evaluation of adductor longus injuries. International Journal of Sports Physical Therapy. https://pmc.ncbi.nlm.nih.gov/articles/PMC12579907/
Oettl, F. C., Zsidai, B., Oeding, J. F., Hirschmann, M. T., Feldt, R., Fendrich, D., Kraeutler, M. J., Winkler, P. W., Szaro, P., Samuelsson, K., & ESSKA Artificial Intelligence Working Group. (2025). Artificial intelligence-assisted analysis of musculoskeletal imaging - A narrative review of the current state of machine learning models. Knee Surgery, Sports Traumatology, Arthroscopy, 33(8), 3032-3038. https://doi.org/10.1002/ksa.12702
Palermi, S., Scavone, A., Anzà, M., Gregorace, E., Vecchiato, M., Bordalo, M., Forster, B. B., Jacobson, J. A., Myers, C., Iriarte, I., & Pedret, C. (2025). Diagnostic ultrasound of muscle injuries: what the sports medicine clinician should know. British Journal of Sports Medicine, 59(24), 1759-1770. https://doi.org/10.1136/bjsports-2025-110255
Park, Y., Han, J., Leikin, S., & Díaz-Gómez, J. L. (2024). Essential point-of-care ultrasound insights for 2024. Seminars in Ultrasound, CT and MRI, 45(1), 22-28. https://doi.org/10.1053/j.sult.2023.12.004
Schroeder, A. N., Kruse, R. C., Rudolph, L., Vidlock, K., Chu, J., Colorado, B. S., Duerson, D., Hatamiya, N., McKeag, I. D., Pierko, E., Prendergast, N., Stafford, C. D. II, Street Callender, S., Yurgil, J., & Waterbrook, A. (2025). Guidelines and checklist for a sports ultrasound curriculum for residents. Current Sports Medicine Reports, 24(7), 219-230. https://doi.org/10.1249/JSR.0000000000001267
Shariatnia, M. M., Bagherieh, S., Semnani, F., et al. (2025). Artificial intelligence applications in musculoskeletal imaging. Current Reviews in Musculoskeletal Medicine, 19(1), 4. https://doi.org/10.1007/s12178-025-09997-0
Shin, Y. R., Yang, J., Lee, Y. H., & Kim, S. (2021). Artificial intelligence in musculoskeletal ultrasound imaging. Ultrasonography, 40(1), 30-44. https://doi.org/10.14366/usg.20080
Soontjens, O., et al. (2026). Achilles tendon ultrasound-derived properties of the dominant and non-dominant side in university basketball athletes. PLOS ONE. https://pmc.ncbi.nlm.nih.gov/articles/PMC12956306/
Staszkiewicz, K., et al. (2025). Smarter, faster, safer: How AI is rewiring sports, performance science, and athlete health. Quality in Sport, 44, 66563. https://doi.org/10.12775/QS.2025.44.66563
Tang, Y., et al. (2025). Ultrasound diagnosis of rotator cuff tears. Journal of Clinical Medicine. https://pmc.ncbi.nlm.nih.gov/articles/PMC12463373/
Wu, K.-T., Chen, P.-C., Chou, W.-Y., Chang, C.-D., & Lien, J.-J. J. (2025). Diagnostic accuracy and interobserver reliability of rotator cuff tear detection with ultrasonography are improved with attentional deep learning. Arthroscopy, 41(8). https://doi.org/10.1016/j.arthro.2024.12.024
Yin, M., et al. (2025). AI-driven medical image analysis for sports injury diagnosis. Diagnostics. https://pmc.ncbi.nlm.nih.gov/articles/PMC12644761/
Zaki, H. A., et al. (2026). Point-of-care ultrasound for diagnosing tendon and ligament injuries of the ankle and wrist: A review. Egyptian Journal of Radiology and Nuclear Medicine. https://link.springer.com/article/10.1186/s43055-026-01722-2
Zhang, M., et al. (2025). Advances in musculoskeletal ultrasound for assistive and sports applications. Biosensors. https://pmc.ncbi.nlm.nih.gov/articles/PMC12085503/
Zhu, L., Xiong, Y., Wu, H., Zhu, L., Tang, Z., Pei, W., Zhou, J., & Xue, Z. (2025). MS-Detector: A hierarchical deep learning method to detect muscle strain using bilateral symmetric ultrasound images of the body. Diagnostics, 15(23), 3087. https://doi.org/10.3390/diagnostics15233087
Longo, U. G., Lalli, A., Nicodemi, G., et al. (2025). Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review. Journal of Experimental Orthopaedics, 12, e70259. https://doi.org/10.1002/jeo2.70259
Prudnikov, Y., et al. (2024). Use of artificial intelligence in the diagnosis and treatment of orthopedic diseases. Wiadomości Lekarskie. https://pubmed.ncbi.nlm.nih.gov/39580822/
Mercurio, M., Denami, F., Vescio, A., Familiari, F., Longo, U. G., Galasso, O., Gasparini, G., on behalf of the Italian Orthopaedic Research Society (IORS), & Dejour, D. H. (2025). Artificial intelligence for the diagnosis and management of patellofemoral instability: A comprehensive review. Diagnostics, 15(22), 2918. https://doi.org/10.3390/diagnostics15222918
Burulday, V., Dag, N., Uslu, A. G., & Yolbas, S. (2026). Shear wave elastography assessment of Achilles tendon stiffness in asymptomatic patients with psoriatic arthritis. Diagnostics, 16(5), 742. https://doi.org/10.3390/diagnostics16050742
Ferreira, V. M. L. M., Pinto, R. Z. A., Simoneau, G., Pogetti, L. S., Resende, R. A., & Ocarino, J. M. (2025). Achilles tendinopathy physical impairments evaluated through clinician-friendly measures: A systematic review with meta-analysis and GRADE recommendations. Brazilian Journal of Physical Therapy, 29(4), 101212. https://doi.org/10.1016/j.bjpt.2025.101212
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