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Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives | Military Medical Research

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  • Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31–8.

    CAS 

    Google Scholar
     

  • Makary MA, Daniel M. Medical error—the third leading cause of death in the US. BMJ. 2016;353:i2139.


    Google Scholar
     

  • Xiang Y, Zhao L, Liu Z, Wu X, Chen J, Long E, et al. Implementation of artificial intelligence in medicine: status analysis and development suggestions. Artif Intell Med. 2020;102:101780.


    Google Scholar
     

  • Malik AT, Khan SN. Predictive modeling in spine surgery. Ann Transl Med. 2019;7(Suppl 5):S173.


    Google Scholar
     

  • Wu E, Wu K, Daneshjou R, Ouyang D, Ho DE, Zou J. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med. 2021;27(4):582–4.

    CAS 

    Google Scholar
     

  • Hui AT, Alvandi LM, Eleswarapu AS, Fornari ED. Artificial intelligence in modern orthopaedics: current and future applications. JBJS Rev. 2022. https://doi.org/10.2106/JBJS.RVW.22.00086.


    Google Scholar
     

  • Charles YP, Lamas V, Ntilikina Y. Artificial intelligence and treatment algorithms in spine surgery. Orthop Traumatol Surg Res. 2023;109(1S):103456.


    Google Scholar
     

  • Gyftopoulos S, Lin D, Knoll F, Doshi AM, Rodrigues TC, Recht MP. Artificial intelligence in musculoskeletal imaging: current status and future directions. AJR Am J Roentgenol. 2019;213(3):506–13.


    Google Scholar
     

  • Ho D, Quake SR, McCabe ERB, Chng WJ, Chow EK, Ding X, et al. Enabling technologies for personalized and precision medicine. Trends Biotechnol. 2020;38(5):497–518.

    CAS 

    Google Scholar
     

  • Patel AA, Schwab JH, Amanatullah DF, Divi SN. AOA critical issues symposium: shaping the impact of artificial intelligence within orthopaedic surgery. J Bone Joint Surg Am. 2023;105(18):1475–9.


    Google Scholar
     

  • Cheng K, Guo Q, He Y, Lu Y, Xie R, Li C, et al. Artificial intelligence in sports medicine: could GPT-4 make human doctors obsolete? Ann Biomed Eng. 2023;51(8):1658–62.


    Google Scholar
     

  • Cabitza F, Locoro A, Banfi G. Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol. 2018;6:75.


    Google Scholar
     

  • Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med. 2023;388(13):1201–8.

    CAS 

    Google Scholar
     

  • Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73–81.


    Google Scholar
     

  • Li Z, Song P, Li G, Han Y, Ren X, Bai L, et al. AI energized hydrogel design, optimization and application in biomedicine. Mater Today Bio. 2024;25:101014.

    CAS 

    Google Scholar
     

  • Chang M, Canseco JA, Nicholson KJ, Patel N, Vaccaro AR. The role of machine learning in spine surgery: the future is mow. Front Surg. 2020;7:54.


    Google Scholar
     

  • Chang TC, Seufert C, Eminaga O, Shkolyar E, Hu JC, Liao JC. Current trends in artificial intelligence application for endourology and robotic surgery. Urol Clin North Am. 2021;48(1):151–60.


    Google Scholar
     

  • LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

    CAS 

    Google Scholar
     

  • Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020;92(4):807–12.


    Google Scholar
     

  • Zhang YP, Zhang XY, Cheng YT, Li B, Teng XZ, Zhang J, et al. Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling. Mil Med Res. 2023;10(1):22.


    Google Scholar
     

  • Zeng S, Wang XL, Yang H. Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer. Mil Med Res. 2024;11(1):77.

    CAS 

    Google Scholar
     

  • Ramkumar PN, Luu BC, Haeberle HS, Karnuta JM, Nwachukwu BU, Williams RJ. Sports medicine and artificial intelligence: a primer. Am J Sports Med. 2022;50(4):1166–74.


    Google Scholar
     

  • Avanzo M, Stancanello J, Pirrone G, Drigo A, Retico A. The evolution of artificial intelligence in medical imaging: from computer science to machine and deep learning. Cancers (Basel). 2024;16(21):3702.

    CAS 

    Google Scholar
     

  • Bai L, Wu Y, Li G, Zhang W, Zhang H, Su J. AI-enabled organoids: construction, analysis, and application. Bioact Mater. 2024;31:525–48.


    Google Scholar
     

  • Myers TG, Ramkumar PN, Ricciardi BF, Urish KL, Kipper J, Ketonis C. Artificial intelligence and orthopaedics: an introduction for clinicians. J Bone Joint Surg Am. 2020;102(9):830–40.


    Google Scholar
     

  • Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–30.


    Google Scholar
     

  • Galbusera F, Casaroli G, Bassani T. Artificial intelligence and machine learning in spine research. JOR Spine. 2019;2(1):e1044.


    Google Scholar
     

  • Chafai N, Luigi B, Sara B, Badaoui B. Emerging applications of machine learning in genomic medicine and healthcare. Crit Rev Clin Lab Sci. 2024;61(2):140–63.

    CAS 

    Google Scholar
     

  • Hassan M, Awan FM, Naz A, deAndrés-Galiana EJ, Alvarez O, Cernea A, et al. Innovations in genomics and big data analytics for personalized medicine and health care: a review. Int J Mol Sci. 2022;23(9):4645.

    CAS 

    Google Scholar
     

  • Kotti M, Duffell LD, Faisal AA, McGregor AH. Detecting knee osteoarthritis and its discriminating parameters using random forests. Med Eng Phys. 2017;43:19–29.


    Google Scholar
     

  • Luu BC, Wright AL, Haeberle HS, Karnuta JM, Schickendantz MS, Makhni EC, et al. Machine learning outperforms logistic regression analysis to predict next-season NHL player injury: an analysis of 2322 players from 2007 to 2017. Orthop J Sports Med. 2020;8(9):2325967120953404.


    Google Scholar
     

  • Ames CP, Smith JS, Pellisé F, Kelly M, Gum JL, Alanay A, et al. Development of predictive models for all individual questions of SRS-22R after adult spinal deformity surgery: a step toward individualized medicine. Eur Spine J. 2019;28(9):1998–2011.


    Google Scholar
     

  • Karhade AV, Schwab JH, Bedair HS. Development of machine learning algorithms for prediction of sustained postoperative opioid prescriptions after total hip arthroplasty. J Arthroplast. 2019;34(10):2272-7.e1.


    Google Scholar
     

  • Klemt C, Laurencin S, Uzosike AC, Burns JC, Costales TG, Yeo I, et al. Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection. Knee Surg Sports Traumatol Arthrosc. 2022;30(8):2582–90.


    Google Scholar
     

  • Merrill RK, Ferrandino RM, Hoffman R, Shaffer GW, Ndu A. Machine learning accurately predicts short-term outcomes following open reduction and internal fixation of ankle fractures. J Foot Ankle Surg. 2019;58(3):410–6.


    Google Scholar
     

  • Potty AG, Potty ASR, Maffulli N, Blumenschein LA, Ganta D, Mistovich RJ, et al. Approaching artificial intelligence in orthopaedics: predictive analytics and machine learning to prognosticate arthroscopic rotator cuff surgical outcomes. J Clin Med. 2023;12(6):2369.


    Google Scholar
     

  • Martin RK, Wastvedt S, Pareek A, Persson A, Visnes H, Fenstad AM, et al. Unsupervised machine learning of the combined Danish and Norwegian knee ligament registers: identification of 5 distinct patient groups with differing ACL revision rates. Am J Sports Med. 2024;52(4):881–91.


    Google Scholar
     

  • Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 2021;13(1):152.


    Google Scholar
     

  • Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019;11(1):70.


    Google Scholar
     

  • Hemke R, Buckless CG, Tsao A, Wang B, Torriani M. Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment. Skeletal Radiol. 2020;49(3):387–95.


    Google Scholar
     

  • Norman B, Pedoia V, Majumdar S. Use of 2D U-net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry. Radiology. 2018;288(1):177–85.


    Google Scholar
     

  • Chung SW, Han SS, Lee JW, Oh K-S, Kim NR, Yoon JP, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop. 2018;89(4):468–73.


    Google Scholar
     

  • Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol. 2018;73(5):439–45.

    CAS 

    Google Scholar
     

  • Lindsey R, Daluiski A, Chopra S, Lachapelle A, Mozer M, Sicular S, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A. 2018;115(45):11591–6.

    CAS 

    Google Scholar
     

  • Borjali A, Chen AF, Muratoglu OK, Morid MA, Varadarajan KM. Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network. J Orthop Res. 2020;38(7):1465–71.


    Google Scholar
     

  • Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9.

    CAS 

    Google Scholar
     

  • Wang S, Peng J, Ma J, Xu J. Protein secondary structure prediction using deep convolutional neural fields. Sci Rep. 2016;6(1):18962.

    CAS 

    Google Scholar
     

  • Wang Z, Combs SA, Brand R, Calvo MR, Xu P, Price G, et al. LM-GVP: an extensible sequence and structure informed deep learning framework for protein property prediction. Sci Rep. 2022;12(1):6832.

    CAS 

    Google Scholar
     

  • Leung K, Zhang B, Tan J, Shen Y, Geras KJ, Babb JS, et al. Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs: data from the osteoarthritis initiative. Radiology. 2020;296(3):584–93.


    Google Scholar
     

  • Burström G, Buerger C, Hoppenbrouwers J, Nachabe R, Lorenz C, Babic D, et al. Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography. J Neurosurg Spine. 2019;31(1):147–54.


    Google Scholar
     

  • Chae J, Kang Y-J, Noh Y. A deep-learning approach for foot-type classification using heterogeneous pressure data. Sensors. 2020;20(16):4481.


    Google Scholar
     

  • Fan G, Liu H, Wu Z, Li Y, Feng C, Wang D, et al. Deep learning–based automatic segmentation of lumbosacral nerves on CT for spinal intervention: a translational study. AJNR Am J Neuroradiol. 2019;40(6):1074–81.

    CAS 

    Google Scholar
     

  • Ghidotti A, Vitali A, Regazzoni D, Cohen MW, Rizzi C. Comparative analysis of convolutional neural network architectures for automated knee segmentation in medical imaging: a performance evaluation. J Comput Inf Sci Eng. 2024;24(5):051005.


    Google Scholar
     

  • Glocker B, Feulner J, Criminisi A, Haynor DR, Konukoglu E, editors. Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. Med Image Comput Comput Assist Interv. 2012;15(Pt 3):590–8.

  • Jamaludin A, Lootus M, Kadir T, Zisserman A, Urban J, Battié MC, et al. ISSLS prize in bioengineering science 2017: automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist. Eur Spine J. 2017;26(5):1374–83.


    Google Scholar
     

  • Lind A, Akbarian E, Olsson S, Nåsell H, Sköldenberg O, Razavian AS, et al. Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system. PLoS ONE. 2021;16(4):e0248809.

    CAS 

    Google Scholar
     

  • Oktay AB, Akgul YS. Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM-based MRF. IEEE Trans Biomed Eng. 2013;60(9):2375–83.


    Google Scholar
     

  • Shah RF, Martinez AM, Pedoia V, Majumdar S, Vail TP, Bini SA. Variation in the thickness of knee cartilage. The use of a novel machine learning algorithm for cartilage segmentation of magnetic resonance images. J Arthroplast. 2019;34(10):2210–5.


    Google Scholar
     

  • Jakubicek R, Chmelik J, Jan J, Ourednicek P, Lambert L, Gavelli G. Learning-based vertebra localization and labeling in 3D CT data of possibly incomplete and pathological spines. Comput Methods Programs Biomed. 2020;183:105081.


    Google Scholar
     

  • Kwan JL, Calder LA, Bowman CL, MacIntyre A, Mimeault R, Honey L, et al. Characteristics and contributing factors of diagnostic error in surgery: analysis of closed medico-legal cases and complaints in Canada. Can J Surg. 2024;67(1):E58.


    Google Scholar
     

  • Federico CA, Trotsyuk AA. Biomedical data science, artificial intelligence, and ethics: navigating challenges in the face of explosive growth. Annu Rev Biomed Data Sci. 2024;7(1):1–14.


    Google Scholar
     

  • Rabie AH, Saleh AI. Diseases diagnosis based on artificial intelligence and ensemble classification. Artif Intell Med. 2024;148:102753.


    Google Scholar
     

  • Guermazi A, Tannoury C, Kompel AJ, Murakami AM, Ducarouge A, Gillibert A, et al. Improving radiographic fracture recognition performance and efficiency using artificial intelligence. Radiology. 2022;302(3):627–36.


    Google Scholar
     

  • Link TM, Pedoia V. Using AI to improve radiographic fracture detection. Radiology. 2022;302(3):637–8.


    Google Scholar
     

  • Wu AM, Bisignano C, James SL, Abady GG, Abedi A, Abu-Gharbieh E, et al. Global, regional, and national burden of bone fractures in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet Healthy Longev. 2021;2(9):e580–92.


    Google Scholar
     

  • Chen K, Stotter C, Klestil T, Nehrer S. Artificial intelligence in orthopedic radiography analysis: a narrative review. Diagnostics. 2022;12(9):2235.


    Google Scholar
     

  • Ajmera P, Kharat A, Botchu R, Gupta H, Kulkarni V. Real-world analysis of artificial intelligence in musculoskeletal trauma. J Clin Orthop Trauma. 2021;22:101573.


    Google Scholar
     

  • Shen L, Gao C, Hu S, Kang D, Zhang Z, Xia D, et al. Using artificial intelligence to diagnose osteoporotic vertebral fractures on plain radiographs. J Bone Miner Res. 2023;38(9):1278–87.


    Google Scholar
     

  • Beyaz S, Açıcı K, Sümer E. Femoral neck fracture detection in X-ray images using deep learning and genetic algorithm approaches. Jt Dis Relat Surg. 2020;31(2):175–83.


    Google Scholar
     

  • Burns JE, Yao J, Summers RM. Vertebral body compression fractures and bone density: automated detection and classification on CT images. Radiology. 2017;284(3):788–97.


    Google Scholar
     

  • Guan B, Zhang G, Yao J, Wang X, Wang M. Arm fracture detection in X-rays based on improved deep convolutional neural network. Comput Electr Eng. 2020;81:106530.


    Google Scholar
     

  • Jones RM, Sharma A, Hotchkiss R, Sperling JW, Hamburger J, Ledig C, et al. Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs. NPJ Digit Med. 2020;3:144.


    Google Scholar
     

  • Langerhuizen DWG, Bulstra AEJ, Janssen SJ, Ring D, Kerkhoffs GMMJ, Jaarsma RL, et al. Is deep learning on par with human observers for detection of radiographically visible and occult fractures of the scaphoid? Clin Orthop Relat Res. 2020;478(11):2653–9.


    Google Scholar
     

  • Li YC, Chen HH, Horng-Shing LuH, Hondar Wu HT, Chang MC, Chou PH. Can a deep-learning model for the automated detection of vertebral fractures approach the performance level of human subspecialists? Clin Orthop Relat Res. 2021;479(7):1598–612.


    Google Scholar
     

  • Niiya A, Murakami K, Kobayashi R, Sekimoto A, Saeki M, Toyofuku K, et al. Development of an artificial intelligence-assisted computed tomography diagnosis technology for rib fracture and evaluation of its clinical usefulness. Sci Rep. 2022;12(1):8363.

    CAS 

    Google Scholar
     

  • Pranata YD, Wang KC, Wang JC, Idram I, Lai JY, Liu JW, et al. Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput Methods Programs Biomed. 2019;171:27–37.


    Google Scholar
     

  • Sato Y, Takegami Y, Asamoto T, Ono Y, Hidetoshi T, Goto R, et al. Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures: a multicenter study. BMC Musculoskelet Disord. 2021;22(1):407.


    Google Scholar
     

  • Yao L, Guan X, Song X, Tan Y, Wang C, Jin C, et al. Rib fracture detection system based on deep learning. Sci Rep. 2021;11(1):23513.

    CAS 

    Google Scholar
     

  • Wu J, Liu N, Li X, Fan Q, Li Z, Shang J, et al. Convolutional neural network for detecting rib fractures on chest radiographs: a feasibility study. BMC Med Imaging. 2023;23(1):18.

    CAS 

    Google Scholar
     

  • Briody H, Hanneman K, Patlas MN. Applications of artificial intelligence in acute thoracic imaging. Can Assoc Radiol J. 2025. https://doi.org/10.1177/08465371251322705.


    Google Scholar
     

  • Rosenberg GS, Cina A, Schiró GR, Giorgi PD, Gueorguiev B, Alini M, et al. Artificial intelligence accurately detects traumatic thoracolumbar fractures on sagittal radiographs. Medicina (Kaunas). 2022;58(8):998.


    Google Scholar
     

  • Murata K, Endo K, Aihara T, Suzuki H, Sawaji Y, Matsuoka Y, et al. Artificial intelligence for the detection of vertebral fractures on plain spinal radiography. Sci Rep. 2020;10(1):20031.

    CAS 

    Google Scholar
     

  • Weng YS, Wang LJ, Huang JQ, Cai LJ. Factors associated with new fractures in adjacent vertebrae after percutaneous vertebroplasty for osteoporotic vertebral compression fractures. Am J Transl Res. 2024;16(11):6972–9.


    Google Scholar
     

  • Lorentzon M, Litsne H, Axelsson KF. The significance of recent fracture location for imminent risk of hip and vertebral fractures—a nationwide cohort study on older adults in Sweden. Osteoporos Int. 2024;35(6):1077–87.


    Google Scholar
     

  • Zech JR, Santomartino SM, Yi PH. Artificial intelligence (AI) for fracture diagnosis: an overview of current products and considerations for clinical adoption, from the AJR special series on AI applications. AJR Am J Roentgenol. 2022;219(6):869–78.


    Google Scholar
     

  • Oakden-Rayner L, Gale W, Bonham TA, Lungren MP, Carneiro G, Bradley AP, et al. Validation and algorithmic audit of a deep learning system for the detection of proximal femoral fractures in patients in the emergency department: a diagnostic accuracy study. Lancet Digit Health. 2022;4(5):e351–8.

    CAS 

    Google Scholar
     

  • Tadavarthi Y, Vey B, Krupinski E, Prater A, Gichoya J, Safdar N, et al. The state of radiology AI: considerations for purchase decisions and current market offerings. Radiol Artif Intell. 2020;2(6):e200004.


    Google Scholar
     

  • Chen M, Cai R, Zhang A, Chi X, Qian J. The diagnostic value of artificial intelligence-assisted imaging for developmental dysplasia of the hip: a systematic review and meta-analysis. J Orthop Surg Res. 2024;19(1):522.


    Google Scholar
     

  • Bradley CS, Verma Y, Maddock CL, Wedge JH, Gargan MF, Kelley SP. A comprehensive nonoperative treatment protocol for developmental dysplasia of the hip in infants. Bone Joint J. 2023;105-B(8):935–42.


    Google Scholar
     

  • Liu C, Xie H, Zhang S, Mao Z, Sun J, Zhang Y. Misshapen pelvis landmark detection with local-global feature learning for diagnosing developmental dysplasia of the hip. IEEE Trans Med Imaging. 2020;39(12):3944–54.


    Google Scholar
     

  • Huang B, Xia B, Qian J, Zhou X, Zhou X, Liu S, et al. Artificial intelligence-assisted ultrasound diagnosis on infant developmental dysplasia of the hip under constrained computational resources. J Ultrasound Med. 2023;42(6):1235–48.


    Google Scholar
     

  • Xu W, Shu L, Gong P, Huang C, Xu J, Zhao J, et al. A deep-learning aided diagnostic system in assessing developmental dysplasia of the hip on pediatric pelvic radiographs. Front Pediatr. 2022;9:785480.


    Google Scholar
     

  • Jaremko JL, Hareendranathan A, Bolouri SES, Frey RF, Dulai S, Bailey AL. AI aided workflow for hip dysplasia screening using ultrasound in primary care clinics. Sci Rep. 2023;13(1):9224.

    CAS 

    Google Scholar
     

  • Ghasseminia S, Lim AKS, Concepcion NDP, Kirschner D, Teo YM, Dulai S, et al. Interobserver variability of hip dysplasia indices on sweep ultrasound for novices, experts, and artificial intelligence. J Pediatr Orthop. 2022;42(4):e315–23.


    Google Scholar
     

  • Libon J, Ng C, Bailey A, Hareendranathan A, Joseph R, Dulai S. Remote diagnostic imaging using artificial intelligence for diagnosing hip dysplasia in infants: results from a mixed-methods feasibility pilot study. Paediatr Child Health. 2023;28(5):285–90.


    Google Scholar
     

  • Kumm J, Roemer FW, Guermazi A, Turkiewicz A, Englund M. Natural history of intrameniscal signal intensity on knee MR images: six years of data from the osteoarthritis initiative. Radiology. 2016;278(1):164–71.


    Google Scholar
     

  • Khan M, Evaniew N, Bedi A, Ayeni OR, Bhandari M. Arthroscopic surgery for degenerative tears of the meniscus: a systematic review and meta-analysis. CMAJ. 2014;186(14):1057–64.


    Google Scholar
     

  • Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Med. 2018;15(11):e1002699.


    Google Scholar
     

  • Fritz B, Marbach G, Civardi F, Fucentese SF, Pfirrmann CWA. Deep convolutional neural network-based detection of meniscus tears: comparison with radiologists and surgery as standard of reference. Skeletal Radiol. 2020;49(8):1207–17.


    Google Scholar
     

  • Pedoia V, Norman B, Mehany SN, Bucknor MD, Link TM, Majumdar S. 3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects. J Magn Reson Imaging. 2019;49(2):400–10.


    Google Scholar
     

  • Sokal PA, Norris R, Maddox TW, Oldershaw RA. The diagnostic accuracy of clinical tests for anterior cruciate ligament tears are comparable but the Lachman test has been previously overestimated: a systematic review and meta-analysis. Knee Surg Sports Traumatol Arthrosc. 2022;30(10):1795.


    Google Scholar
     

  • Nyland J. The ACL Café Menu: individualised treatment of anterior cruciate ligament injuries—from prevention to conservative treatment, repair and reconstruction. Knee Surg Sports Traumatol Arthrosc. 2025.

  • Štajduhar I, Mamula M, Miletić D, Ünal G. Semi-automated detection of anterior cruciate ligament injury from MRI. Comput Methods Programs Biomed. 2017;140:151–64.


    Google Scholar
     

  • Richardson ML. MR protocol optimization with deep learning: a proof of concept. Curr Probl Diagn Radiol. 2021;50(2):168–74.


    Google Scholar
     

  • Tran A, Lassalle L, Zille P, Guillin R, Pluot E, Adam C, et al. Deep learning to detect anterior cruciate ligament tear on knee MRI: multi-continental external validation. Eur Radiol. 2022;32(12):8394–403.


    Google Scholar
     

  • Liu F, Guan B, Zhou Z, Samsonov A, Rosas H, Lian K, et al. Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning. Radiol Artif Intell. 2019;1(3):180091.


    Google Scholar
     

  • Zeng W, Ismail SA, Pappas E. Detecting the presence of anterior cruciate ligament injury based on gait dynamics disparity and neural networks. Artif Intell Rev. 2020;53(5):3153–76.


    Google Scholar
     

  • Li X, Huang H, Wang J, Yu Y, Ao Y. The analysis of plantar pressure data based on multimodel method in patients with anterior cruciate ligament deficiency during walking. Biomed Res Int. 2016;2016(1):7891407.


    Google Scholar
     

  • Wu R, Guo Y, Chen Y, Zhang J. Osteoarthritis burden and inequality from 1990 to 2021: a systematic analysis for the global burden of disease study 2021. Sci Rep. 2025;15(1):8305.

    CAS 

    Google Scholar
     

  • Conrozier T, Brandt K, Piperno M, Mathieu P, Merle-Vincent F, Vignon E. Reproducibility and sensitivity to change of a new method of computer measurement of joint space width in hip osteoarthritis. Performance of three radiographic views obtained at a 3-year interval. Osteoarthr Cartil. 2009;17(7):864–70.

    CAS 

    Google Scholar
     

  • Mulford KL, Kaji ES, Grove AF, Saniei S, Girod-Hoffman M, Maradit-Kremers H, et al. A deep learning tool for minimum joint space width calculation on antero-posterior knee radiographs. J Arthroplast. 2025. https://doi.org/10.1016/j.arth.2025.01.038.


    Google Scholar
     

  • Üreten K, Arslan T, Gültekin KE, Demir AND, Özer HF, Bilgili Y. Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods. Skeletal Radiol. 2020;49(9):1369–74.


    Google Scholar
     

  • Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S. Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci Rep. 2018;8(1):1727.


    Google Scholar
     

  • Norman B, Pedoia V, Noworolski A, Link TM, Majumdar S. Applying densely connected convolutional neural networks for staging osteoarthritis severity from plain radiographs. J Digit Imaging. 2019;32(3):471–7.


    Google Scholar
     

  • Pedoia V, Lee J, Norman B, Link TM, Majumdar S. Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire osteoarthritis initiative baseline cohort. Osteoarthr Cartil. 2019;27(7):1002–10.

    CAS 

    Google Scholar
     

  • Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70–6.


    Google Scholar
     

  • Hughes TM, Dossett LA, Hawley ST, Telem DA. Recognizing heuristics and bias in clinical decision-making. Ann Surg. 2020;271(5):813–4.


    Google Scholar
     

  • Jacofsky DJ, Allen M. Robotics in arthroplasty: a comprehensive review. J Arthroplast. 2016;31(10):2353–63.


    Google Scholar
     

  • McDonnell JM, Ahern DP, Doinn TÓ, Gibbons D, Rodrigues KN, Birch N, et al. Surgeon proficiency in robot-assisted spine surgery. Bone Joint J. 2020;102-B(5):568–72.


    Google Scholar
     

  • Murphy MP, Brown NM. CORR synthesis: when should the orthopaedic surgeon use artificial intelligence, machine learning, and deep learning?. Clin Orthop Relat Res. 2021;479(7):1497–505.


    Google Scholar
     

  • Chen X, Li S, Liu X, Wang Y, Ma R, Zhang Y, et al. Acetabular diameter assessment and three-dimensional simulation for acetabular reconstruction in dysplastic hips. J Arthroplast. 2023;38(8):1551–8.


    Google Scholar
     

  • Darwood A, Hurst SA, Villatte G, Tatti F, El Daou H, Reilly P, et al. Novel robotic technology for the rapid intraoperative manufacture of patient-specific instrumentation allowing for improved glenoid component accuracy in shoulder arthroplasty: a cadaveric study. J Shoulder Elbow Surg. 2022;31(3):561–70.


    Google Scholar
     

  • Gebremeskel M, Shafiq B, Uneri A, Sheth N, Simmerer C, Zbijewski W, et al. Quantification of manipulation forces needed for robot-assisted reduction of the ankle syndesmosis: an initial cadaveric study. Int J Comput Assist Radiol Surg. 2022;17(12):2263–7.


    Google Scholar
     

  • Giorgini A, Tarallo L, Novi M, Porcellini G. Computer-assisted surgery in reverse shoulder arthroplasty: early experience. Indian J Orthop. 2021;55(4):1003–8.


    Google Scholar
     

  • Kayani B, Konan S, Tahmassebi J, Pietrzak JRT, Haddad FS. Robotic-arm assisted total knee arthroplasty is associated with improved early functional recovery and reduced time to hospital discharge compared with conventional jig-based total knee arthroplasty. Bone Joint J. 2018;100(B(7)):930–7.


    Google Scholar
     

  • Rossi SMP, Sangaletti R, Andriollo L, Matascioli L, Benazzo F. The use of a modern robotic system for the treatment of severe knee deformities. Technol Health Care. 2024;32:3737–46.


    Google Scholar
     

  • Sakakibara Y, Teramoto A, Takagi T, Yamakawa S, Shoji H, Okada Y, et al. Effects of the ankle flexion angle during anterior talofibular ligament reconstruction on ankle kinematics, laxity, and in situ forces of the reconstructed graft. Foot Ankle Int. 2022;43(5):725–32.


    Google Scholar
     

  • Yang G, Liu D, Zhou G, Wang Q, Zhang X. Robot-assisted anterior cruciate ligament reconstruction based on three-dimensional images. J Orthop Surg Res. 2024;19(1):246.


    Google Scholar
     

  • Twomey-Kozak J, Hurley E, Levin J, Anakwenze O, Klifto C. Technological innovations in shoulder replacement: current concepts and the future of robotics in total shoulder arthroplasty. J Shoulder Elbow Surg. 2023;32(10):2161–71.


    Google Scholar
     

  • Herzog MM, Kerr ZY, Marshall SW, Wikstrom EA. Epidemiology of ankle sprains and chronic ankle instability. J Athl Train. 2019;54(6):603–10.


    Google Scholar
     

  • Peiffer M, Lewis L, Xie K, Guild TT, Ashkani-Esfahani S, Kwon JY. The influence of talar displacement on articular contact mechanics: a 3D finite element analysis study using weightbearing computed tomography. Foot Ankle Int. 2024;45(4):393–405.


    Google Scholar
     

  • Gregersen MG, Dalen AF, Skrede AL, Bjelland Ø, Nilsen FA, Molund M. Effects of fibular plate fixation on ankle stability in a Weber B fracture model with partial deltoid ligament sectioning. Foot Ankle Int. 2024;45(6):641–7.


    Google Scholar
     

  • Spindler FT, Gaube FP, Böcker W, Polzer H, Baumbach SF. Value of intraoperative 3D imaging on the quality of reduction of the distal tibiofibular joint when using a suture-button system. Foot Ankle Int. 2022;44(1):54–61.


    Google Scholar
     

  • Bajorath J, Kearnes S, Walters WP, Meanwell NA, Georg GI, Wang S. Artificial intelligence in drug discovery: into the great wide open. J Med Chem. 2020;63(16):8651–2.

    CAS 

    Google Scholar
     

  • Smalley E. AI-powered drug discovery captures pharma interest. Nat Biotechnol. 2017;35(7):604–5.

    CAS 

    Google Scholar
     

  • Brown FK, Sherer EC, Johnson SA, Holloway MK, Sherborne BS. The evolution of drug design at Merck Research Laboratories. J Comput Aided Mol Des. 2017;31(3):255–66.

    CAS 

    Google Scholar
     

  • Gangwal A, Lavecchia A. Unleashing the power of generative AI in drug discovery. Drug Discov Today. 2024;29(6):103992.

    CAS 

    Google Scholar
     

  • Lowe D. AI designs organic syntheses. Nature. 2018;555(7698):592–3.

    CAS 

    Google Scholar
     

  • Schneider G. Automating drug discovery. Nat Rev Drug Discov. 2018;17(2):97–113.

    CAS 

    Google Scholar
     

  • Gligorijević V, Renfrew PD, Kosciolek T, Leman JK, Berenberg D, Vatanen T, et al. Structure-based protein function prediction using graph convolutional networks. Nat Commun. 2021;12(1): 3168.


    Google Scholar
     

  • Spencer M, Eickholt J, Cheng J. A deep learning network approach to ab initio protein secondary structure prediction. IEEE/ACM Trans Comput Biol Bioinform. 2015;12(1):103–12.

    CAS 

    Google Scholar
     

  • Yuan L, Ma Y, Liu Y. Ensemble deep learning models for protein secondary structure prediction using bidirectional temporal convolution and bidirectional long short-term memory. Front Bioeng Biotechnol. 2023;11:1051268.


    Google Scholar
     

  • Offensperger F, Tin G, Duran-Frigola M, Hahn E, Dobner S, Ende CWa, et al. Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells. Science. 2024;384(6694):eadk5864.

    CAS 

    Google Scholar
     

  • Wang F, Liu D, Wang H, Luo C, Zheng M, Liu H, et al. Computational screening for active compounds targeting protein sequences: methodology and experimental validation. J Chem Inf Model. 2011;51(11):2821–8.

    CAS 

    Google Scholar
     

  • Ren F, Aliper A, Chen J, Zhao H, Rao S, Kuppe C, et al. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models. Nat Biotechnol. 2025;43(1):63–75.

    CAS 

    Google Scholar
     

  • Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev. 2019;119(18):10520–94.

    CAS 

    Google Scholar
     

  • Averta G, Della Santina C, Valenza G, Bicchi A, Bianchi M. Exploiting upper-limb functional principal components for human-like motion generation of anthropomorphic robots. J Neuroeng Rehabil. 2020;17(1):63.


    Google Scholar
     

  • Zhao Y, Liang C, Gu Z, Zheng Y, Wu Q. A new design scheme for intelligent upper limb rehabilitation training robot. Int J Environ Res Public Health. 2020;17(8):2948.


    Google Scholar
     

  • Miller-Jackson TM, Natividad RF, Lim DYL, Hernandez-Barraza L, Ambrose JW, Yeow RC-H. A wearable soft robotic exoskeleton for hip flexion rehabilitation. Front Robot AI. 2022;9:835237.


    Google Scholar
     

  • Rossi SMP, Panzera RM, Sangaletti R, Andriollo L, Giudice L, Lecci F, et al. Problems and opportunities of a smartphone-based care management platform: application of the Wald principles to a survey-based analysis of patients’ perception in a pilot center. Healthcare. 2024;12(2):153.


    Google Scholar
     

  • Chen ZH, Lin L, Wu CF, Li CF, Xu RH, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond). 2021;41(11):1100–15.


    Google Scholar
     

  • Sánchez de la Nava AM, Atienza F, Bermejo J, Fernández-Avilés F. Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation. Am J Physiol Heart Circ Physiol. 2021;320(4):H1337–47.


    Google Scholar
     

  • Ames CP, Smith JS, Pellisé F, Kelly M, Alanay A, Acaroğlu E, et al. Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value. Spine. 2019;44(13):915–26.


    Google Scholar
     

  • Bertsimas D, Masiakos PT, Mylonas KS, Wiberg H. Prediction of cervical spine injury in young pediatric patients: an optimal trees artificial intelligence approach. J Pediatr Surg. 2019;54(11):2353–7.


    Google Scholar
     

  • Cattaneo A, Ghidotti A, Catellani F, Fiorentino G, Vitali A, Regazzoni D, et al. Motion acquisition of gait characteristics one week after total hip arthroplasty: a factor analysis. Arch Orthop Trauma Surg. 2024;144(5):2347–56.


    Google Scholar
     

  • Johnson WR, Mian A, Lloyd DG, Alderson JA. On-field player workload exposure and knee injury risk monitoring via deep learning. J Biomech. 2019;93:185–93.


    Google Scholar
     

  • Pellisé F, Serra-Burriel M, Smith JS, Haddad S, Kelly MP, Vila-Casademunt A, et al. Development and validation of risk stratification models for adult spinal deformity surgery. J Neurosurg Spine. 2019;31(4):587–99.


    Google Scholar
     

  • Shohat N, Goswami K, Tan TL, Yayac M, Soriano A, Sousa R, et al. 2020 frank stinchfield award: identifying who will fail following irrigation and debridement for prosthetic joint infection. Bone Joint J. 2020;102-B((7 Supple B)):11–9.


    Google Scholar
     

  • Taborri J, Molinaro L, Santospagnuolo A, Vetrano M, Vulpiani MC, Rossi S. A machine-learning approach to measure the anterior cruciate ligament injury risk in female basketball players. Sensors (Basel). 2021;21(9):3141.


    Google Scholar
     

  • Tamimi I, Ballesteros J, Lara AP, Tat J, Alaqueel M, Schupbach J, et al. A prediction model for primary anterior cruciate ligament injury using artificial intelligence. Orthop J Sports Med. 2021;2021(9):23259671211027544.


    Google Scholar
     

  • Pedoia V, Lansdown DA, Zaid M, McCulloch CE, Souza R, Ma CB, et al. Three-dimensional MRI-based statistical shape model and application to a cohort of knees with acute ACL injury. Osteoarthr Cartil. 2015;23(10):1695–703.

    CAS 

    Google Scholar
     

  • Alzubaidi L, Al-Dulaimi K, Salhi A, Alammar Z, Fadhel MA, Albahri AS, et al. Comprehensive review of deep learning in orthopaedics: applications, challenges, trustworthiness, and fusion. Artif Intell Med. 2024;155:102935.


    Google Scholar
     

  • Himeur Y, Al-Maadeed S, Kheddar H, Al-Maadeed N, Abualsaud K, Mohamed A, et al. Video surveillance using deep transfer learning and deep domain adaptation: towards better generalization. Eng Appl Artif Intell. 2023;119:105698.


    Google Scholar
     

  • Jakubovitz D, Giryes R, Rodrigues MRD. Generalization error in deep learning. In: Boche H, Caire G, Calderbank R, Kutyniok G, Mathar R, Petersen P, editors. Compressed sensing and its applications: third international MATHEON conference 2017. Cham: Springer; 2019. p. 153–93.


    Google Scholar
     

  • Roy S, Pal D, Meena T. Explainable artificial intelligence to increase transparency for revolutionizing healthcare ecosystem and the road ahead. Netw Model Anal Health Inform Bioinforma. 2023;13(1):4.


    Google Scholar
     

  • Arnab A, Miksik O, Torr PHS. On the robustness of semantic segmentation models to adversarial attacks. IEEE Trans Pattern Anal Mach Intell. 2020;42(12):3040–53.


    Google Scholar
     

  • Oliveira e Carmo L, van den Merkhof A, Olczak J, Gordon M, Jutte PC, Jaarsma RL, et al. An increasing number of convolutional neural networks for fracture recognition and classification in orthopaedics. Bone Jt Open. 2021;2(10):879–85.

  • George MP, Bixby S. Frequently missed fractures in pediatric trauma: a pictorial review of plain film radiography. Radiol Clin North Am. 2019;57(4):843–55.


    Google Scholar
     

  • Allen B, Agarwal S, Coombs L, Wald C, Dreyer K. 2020 ACR data science institute artificial intelligence survey. J Am Coll Radiol. 2021;18(8):1153–9.


    Google Scholar
     

  • Roy S, Meena T, Lim SJ. Demystifying supervised learning in healthcare 4.0: a new reality of transforming diagnostic medicine. Diagnostics (Basel). 2022;12(10):2549.


    Google Scholar
     

  • Naik N, Hameed BMZ, Shetty DK, Swain D, Shah M, Paul R, et al. Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility?. Front Surg. 2022;9:862322.


    Google Scholar
     

  • Dallora AL, Anderberg P, Kvist O, Mendes E, Diaz Ruiz S, Sanmartin BJ. Bone age assessment with various machine learning techniques: a systematic literature review and meta-analysis. PLoS One. 2019;14(7):e0220242.

    CAS 

    Google Scholar
     



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    AI Insights

    AI prompt injection gets real — with macros the latest hidden threat

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    “Attackers conceal instructions via ultra-small fonts, background-matched text, ASCII smuggling using Unicode Tags, macros that inject payloads at parsing time, and even file metadata (e.g., DOCX custom properties, PDF/XMP, EXIF),” Granoša explained. “These vectors evade human review yet are fully parsed and executed by LLMs, enabling indirect prompt injection.”

    Countermeasures

    Justin Endres, head of data security at cybersecurity vendor Seclore, argued that security leaders can’t rely on legacy tools alone to defend against malicious prompts that turn “everyday files into Trojan horses for AI systems.”

    “[Security leaders] need layered defenses that sanitize content before it ever reaches an AI parser, enforce strict guardrails around model inputs, and keep humans in the loop for critical workflows,” Endres advised. “Otherwise, attackers will be the ones writing the prompts that shape your AI’s behavior.”



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    Mapping the power of AI across the patient journey

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    Artificial intelligence (AI) is rapidly transforming clinical care, offering healthcare leaders new tools to improve workflows through automation and enhance patient outcomes with more accurate diagnoses and personalized treatments. This resource provides a framework for understanding how AI is applied across the patient journey, from pre-visit interactions to post‑visit monitoring and ongoing care. It focuses on actionable use cases to help healthcare organizations evaluate AI technologies holistically, balance innovation with feasibility, and navigate the evolving landscape of AI in healthcare.

    For a deeper exploration of any specific use case featured in this infographic, check out our comprehensive compendium. It offers detailed insights into these technologies, including their benefits, implementation considerations, and evolving role in healthcare.



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    West Alabama school district looks to strengthen AI policy

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    TUSCALOOSA, Ala. (WBRC) – One west Alabama school district is working to update its policy on artificial intelligence (AI).

    Tuscaloosa City Schools wants to hear from parents when it comes to how they handle AI, a growing system that continues to evolve.

    The school district has a committee studying best-use practices and a major part of that study is surveying parents on how they think AI could be strengthened to improve teaching and learning.

    Central High School English teacher Rachael James is the first to admit just the mere mention of AI intimidated her a bit.

    “There is definitely that intimidation factor,” said James.

    But AI is here to stay, and James felt the best way to tackle it is to confront it head on with crystal clarity.

    James learned early on that for teachers, AI is simply another resource – another avenue – to find apps that help do their jobs better.

    “AI allows us to create different tools to address different learning styles and it also makes some of the legwork in education a little easier with creating lesson plans we need to run our classes smoothly,” said James.

    But like any new thing, there is a chance it could be abused and harmful.

    “Safety and privacy are most important,” said Tuscaloosa City Schools Superintendent Dr. Mike Daria.

    Dr. Daria says the school district is sending out surveys to parents to get their feedback on how to make the use of AI better, stronger and safer in the classroom.

    “We know it’s evolving very quickly and we believe it’s important to have input from our parents on the way we use it. A big part of that is AI literacy so our students can understand AI, navigate it, interpret it, discern what it is and what it’s not,” said Dr. Daria.

    But what about the teacher-student relationship? Could artificial intelligence damage the synergy?

    “There is that fear. However, being able to educate, even if computers take over, there still has to be human engagement in some shape, form or fashion,” said James.

    Either way, the future is here and James is on the front line and mastering it along the way.

    “The goal is not to be afraid of AI,” said James.

    School district leaders said parents have until September 26 to complete the survey.

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