Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022;28(1):31–8.
Google Scholar
Makary MA, Daniel M. Medical error—the third leading cause of death in the US. BMJ. 2016;353:i2139.
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.
Malik AT, Khan SN. Predictive modeling in spine surgery. Ann Transl Med. 2019;7(Suppl 5):S173.
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.
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.
Charles YP, Lamas V, Ntilikina Y. Artificial intelligence and treatment algorithms in spine surgery. Orthop Traumatol Surg Res. 2023;109(1S):103456.
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.
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.
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.
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.
Cabitza F, Locoro A, Banfi G. Machine learning in orthopedics: a literature review. Front Bioeng Biotechnol. 2018;6:75.
Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med. 2023;388(13):1201–8.
Google Scholar
Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73–81.
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.
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.
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.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
Google Scholar
Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc. 2020;92(4):807–12.
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.
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.
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.
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.
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.
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.
Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–30.
Galbusera F, Casaroli G, Bassani T. Artificial intelligence and machine learning in spine research. JOR Spine. 2019;2(1):e1044.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019;11(1):70.
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.
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.
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.
Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol. 2018;73(5):439–45.
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.
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.
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.
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.
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.
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.
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.
Chae J, Kang Y-J, Noh Y. A deep-learning approach for foot-type classification using heterogeneous pressure data. Sensors. 2020;20(16):4481.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Rabie AH, Saleh AI. Diseases diagnosis based on artificial intelligence and ensemble classification. Artif Intell Med. 2024;148:102753.
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.
Link TM, Pedoia V. Using AI to improve radiographic fracture detection. Radiology. 2022;302(3):637–8.
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.
Chen K, Stotter C, Klestil T, Nehrer S. Artificial intelligence in orthopedic radiography analysis: a narrative review. Diagnostics. 2022;12(9):2235.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Richardson ML. MR protocol optimization with deep learning: a proof of concept. Curr Probl Diagn Radiol. 2021;50(2):168–74.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Google Scholar
Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70–6.
Hughes TM, Dossett LA, Hawley ST, Telem DA. Recognizing heuristics and bias in clinical decision-making. Ann Surg. 2020;271(5):813–4.
Jacofsky DJ, Allen M. Robotics in arthroplasty: a comprehensive review. J Arthroplast. 2016;31(10):2353–63.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Herzog MM, Kerr ZY, Marshall SW, Wikstrom EA. Epidemiology of ankle sprains and chronic ankle instability. J Athl Train. 2019;54(6):603–10.
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.
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.
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.
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.
Google Scholar
Smalley E. AI-powered drug discovery captures pharma interest. Nat Biotechnol. 2017;35(7):604–5.
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.
Google Scholar
Gangwal A, Lavecchia A. Unleashing the power of generative AI in drug discovery. Drug Discov Today. 2024;29(6):103992.
Google Scholar
Lowe D. AI designs organic syntheses. Nature. 2018;555(7698):592–3.
Google Scholar
Schneider G. Automating drug discovery. Nat Rev Drug Discov. 2018;17(2):97–113.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Google Scholar
