BhatiaV.Exploring machine learning applications in sports injury prediction analysis. International J Res Appl Sci Eng Technol. 2024;12(10):1300-1307. doi:10.22214/ijraset.2024.64850
2.
BullockGSHughesTSergeantJCCallaghanMJRileyRCollinsG.Methods matter: clinical prediction models will benefit sports medicine practice, but only if they are properly developed and validated. Br J Sports Med. 2021;55(23):1319-1321. doi:10.1136/bjsports-2021-104329
3.
ChaabeniAKalaiABen SaadH, et al. Observed events frequencies and associated factors among soccer players during the 2021 African Cup of Nations competition: A video-based exploratory analysis. F1000Research. 2025;13(392)doi:10.12688/f1000research.148161.3.
4.
ChaabeniASlamaMMKalaiA, et al. A bibliometric analysis of the 100 most-cited articles on soccer injuries. Cureus. 2025;17(4):e82636. doi:10.7759/cureus.82636
5.
DergaaIZakhamaLDziriCBen SaadH.Enhancing scholarly discourse in the age of artificial intelligence: A guided approach to effective peer review process. Tunis Med. 2023;101(10):721-726.
6.
EdouardPDandrieuxP-EIatropoulosS, et al. Injuries in athletics (track and field): A narrative review presenting the current problem of injuries. Dtsch Z Sportmed. 2024;75(4):132-141. doi:10.5960/dzsm.2024.601
7.
EiraleCTolJLFarooqASmileyFChalabiH.Low injury rate strongly correlates with team success in Qatari professional football. Br J Sports Med. Aug2013;47(12):807-8. doi:10.1136/bjsports-2012-091040
8.
FagherKDahlstromOJacobssonJTimpkaTLexellJ.Prevalence of sports-related injuries and illnesses in Paralympic athletes. PM R. 2020;12(3):271-280. doi:10.1002/pmrj.12211
9.
GuelmamiNFekih-RomdhaneFMechraouiOBragazziNL.Injury prevention, optimized training and rehabilitation: How is AI reshaping the field of sports medicine. New Asian J Med. 2023;1(1):30-34. doi:10.61838/kman.najm.1.1.6
10.
GurauTVGurauGMusatCL, et al. Epidemiology of injuries in professional and amateur football men (Part II). J Clin Med. 29 2023;12(19)doi:10.3390/jcm12196293
11.
GurauTVGurauGVoinescuDC, et al. Epidemiology of injuries in men’s professional and amateur football (Part I). J Clin Med. 26 2023;12(17)doi:10.3390/jcm12175569
12.
Harishbhai TilalaMKumar ChenchalaPChoppadandiA, et al. Ethical considerations in the use of artificial intelligence and machine learning in health care: A comprehensive review. Cureus. 2024;16(6):e62443. doi:10.7759/cureus.62443
13.
JiangFJiangYZhiH, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-243. doi:10.1136/svn-2017-000101
14.
KakavasGMalliaropoulosNPrunaRMaffulliN.Artificial intelligence: A tool for sports trauma prediction. Injury. 2020;51 Suppl3:S63-S65. doi:10.1016/j.injury.2019.08.033
15.
LakshakarPSathePSatheA.Common sports injury in football players: A review. Int J Sci Healthc Res. 2022;7(2):26-24.
16.
LeckeyCvan DykNDohertyCLawlorADelahuntE.Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis. Br J Sports Med. 2025;59(7):491-500. doi:10.1136/bjsports-2024-108576
17.
MusatCLMereutaCNechitaA, et al. Diagnostic applications of AI in sports: A comprehensive review of injury risk prediction methods. Diagnostics (Basel). Nov102024;14(22)doi:10.3390/diagnostics14222516
18.
RossiAPappalardoLCintiaPIaiaFMFernandezJMedinaD.Effective injury forecasting in soccer with GPS training data and machine learning. PLoS One. 2018;13(7):e0201264. doi:10.1371/journal.pone.0201264
19.
TopolEJ.High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi:10.1038/s41591-018-0300-7
20.
TuylsKOmidshafieiSMullerP, et al. Game Plan: What AI can do for football, and what football can do for AI. J Artif Intell Res. 2021;71:41-88. doi:10.1613/jair.1.12505
21.
VincentHKBrownsteinMVincentKR.Injury prevention, safe training techniques, rehabilitation, and return to sport in trail runners. Arthrosc Sports Med Rehabil. 2022;4(1):e151-e162. doi:10.1016/j.asmr.2021.09.032
