Artificial intelligence in orthopaedic surgery: exploring its applications, limitations, and future direction

Convolutional neural networks: an overview and application in radiology

Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682

Artificial intelligence in surgery: promises and perils

Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002693 · Cited by 1,468

Definition of Periprosthetic Joint Infection

Parvizi, J., & Gehrke, T. A. (2014) · The Journal of Arthroplasty · doi:10.1016/j.arth.2014.03.009 · Cited by 916

Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist.

Norgeot, B., Quer, G., Beaulieu-Jones, B. K., Torkamani, A., Dias, R., Gianfrancesco, M., Arnaout, R., Kohane, I. S., Saria, S., Topol, E., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1041-y · Cited by 665

A standardized method for assessment of elbow function

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Artificial Intelligence and Orthopaedics

Myers, T. G., Ramkumar, P. N., Ricciardi, B. F., Urish, K. L., Kipper, J., & Ketonis, C. (2020) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.19.01128 · Cited by 254

Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative

Leung, K., Zhang, B., Tan, J., Shen, Y., Geras, K. J., Babb, J. S., Cho, K., Chang, G. A., & Deniz, C. M. (2020) · Radiology · doi:10.1148/radiol.2020192091 · Cited by 251

The Digital Twin in Medicine: A Key to the Future of Healthcare?

Sun, T., He, X., Song, X., Shu, L., & Li, Z. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.907066 · Cited by 213

Remote Patient Monitoring Using Mobile Health for Total Knee Arthroplasty: Validation of a Wearable and Machine Learning–Based Surveillance Platform

Ramkumar, P. N., Haeberle, H. S., Ramanathan, D., Cantrell, W. A., Navarro, S. M., Mont, M. A., Bloomfield, M. R., & Patterson, B. M. (2019) · The Journal of Arthroplasty · doi:10.1016/j.arth.2019.05.021 · Cited by 209

What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review

Langerhuizen, D. W. G., Janssen, S. J., Mallee, W. H., van den Bekerom, M. P. J., Ring, D., Kerkhoffs, G. M. M. J., Jaarsma, R. L., & Doornberg, J. N. (2019) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000000848 · Cited by 197

Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions

Gyftopoulos, S., Lin, D. J., Knöll, F., Doshi, A. M., Rodrigues, T. C., & Recht, M. P. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.19.21117 · Cited by 170

Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images

Pranata, Y. D., Wang, K.-C., Wang, J., Idram, I., Lai, J., Liu, J., & Hsieh, I.-H. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.02.006 · Cited by 159

Can Machine Learning Methods Produce Accurate and Easy-to-use Prediction Models of 30-day Complications and Mortality After Knee or Hip Arthroplasty?

Harris, A. H. S., Kuo, A. C., Weng, Y., Trickey, A. W., Bowe, T. R., & Giori, N. J. (2019) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000000601 · Cited by 147

Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal

Olczak, J., Pavlopoulos, J., Prijs, J., IJpma, F. F. A., Doornberg, J. N., Lundström, C. F., Hedlund, J., & Gordon, M. (2021) · Acta Orthopaedica · doi:10.1080/17453674.2021.1918389 · Cited by 145

Current Concepts in Alignment in Total Knee Arthroplasty

Karasavvidis, T., Moldenhauer, C. A. P., Haddad, F. S., Hirschmann, M. T., Pagnano, M. W., & Vigdorchik, J. M. (2023) · The Journal of Arthroplasty · doi:10.1016/j.arth.2023.01.060 · Cited by 134

Functional alignment achieves soft-tissue balance in total knee arthroplasty as measured with quantitative sensor-guided technology

Chang, J. S., Kayani, B., Wallace, C. N., & Haddad, F. S. (2021) · The Bone & Joint Journal · doi:10.1302/0301-620x.103b.bjj-2020-0940.r1 · Cited by 131

Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network

Borjali, A., Chen, A. F., Muratoglu, O. K., Morid, M. A., & Varadarajan, K. M. (2020) · Journal of Orthopaedic Research® · doi:10.1002/jor.24617 · Cited by 123

A prospective randomized controlled trial comparing the systemic inflammatory response in conventional jig-based total knee arthroplasty versus robotic-arm assisted total knee arthroplasty

Kayani, B., Tahmassebi, J. E., Ayuob, A., Konan, S., Oussedik, S. I., & Haddad, F. S. (2020) · The Bone & Joint Journal · doi:10.1302/0301-620x.103b1.bjj-2020-0602.r2 · Cited by 122

Applications of artificial intelligence in orthopaedic surgery

Farhadi, F., Barnes, M. R., Sugito, H. R., Sin, J. M., Henderson, E. R., & Levy, J. (2022) · Frontiers in Medical Technology · doi:10.3389/fmedt.2022.995526 · Cited by 117

Comparative Cost-effectiveness of Robot-assisted and Standard Laparoscopic Prostatectomy as Alternatives to Open Radical Prostatectomy for Treatment of Men with Localised Prostate Cancer: A Health Technology Assessment from the Perspective of the UK National Health Service

Close, A. J., Robertson, C., Rushton, S. P., Shirley, M., Vale, L. D., Ramsay, C. R., & Pickard, R. S. (2013) · European Urology · doi:10.1016/j.eururo.2013.02.040 · Cited by 116

Advancing Surgical Education: The Use of Artificial Intelligence in Surgical Training

Guerrero, D. T., Asaad, M., Rajesh, A., Hassan, A. M., & Butler, C. E. (2022) · The American Surgeon · doi:10.1177/00031348221101503 · Cited by 116

Effect of Remote Monitoring on Discharge to Home, Return to Activity, and Rehospitalization After Hip and Knee Arthroplasty

Mehta, S. J., Hume, E. L., Troxel, A. B., Reitz, C., Norton, L. A., Lacko, H., McDonald, C., Freeman, J. D., Marcus, N., Volpp, K. G. M., et al. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.28328 · Cited by 108

Artificial intelligence in orthopedic surgery: evolution, current state and future directions

Kurmis, A. P., & Ianunzio, J. R. (2022) · Arthroplasty · doi:10.1186/s42836-022-00112-z · Cited by 105

Assuring the long-term total joint arthroplasty

Kayani, B., Konan, S., Thakrar, R. R., Huq, S. S., & Haddad, F. S. (2019) · The Bone & Joint Journal · doi:10.1302/0301-620x.101b1.bjj-2018-0377.r1 · Cited by 100

Predicting Surgical Complications in Patients Undergoing Elective Adult Spinal Deformity Procedures Using Machine Learning

Kim, J., Arvind, V., Oermann, E. K., Kaji, D. A., Ranson, W. A., Ukogu, C. O., Hussain, A. K., Caridi, J. M., & Cho, S. K.-W. (2018) · Spine Deformity · doi:10.1016/j.jspd.2018.03.003 · Cited by 98

The Cost-Effectiveness of Robotic-Assisted Versus Manual Total Knee Arthroplasty: A Markov Model–Based Evaluation

Rajan, P. V., Khlopas, A., Klika, A., Molloy, R., Krebs, V., & Piuzzi, N. S. (2022) · Journal of the American Academy of Orthopaedic Surgeons · doi:10.5435/jaaos-d-21-00309 · Cited by 98

Femoral neck fracture detection in X-ray images using deep learning and genetic algorithm approaches

Beyaz, S. (2020) · Joint Diseases and Related Surgery · doi:10.5606/ehc.2020.72163 · Cited by 96

The learning curve of robotic-arm assisted acetabular cup positioning during total hip arthroplasty

Kayani, B., Konan, S., Huq, S. S., Ibrahim, M. S., Ayuob, A., & Haddad, F. S. (2019) · Hip International · doi:10.1177/1120700019889334 · Cited by 94

Advances and innovations in total hip arthroplasty

Fontalis, A., Epinette, J.-A., Thaler, M. R., Zagra, L., Khanduja, V., & Haddad, F. S. (2021) · SICOT-J · doi:10.1051/sicotj/2021025 · Cited by 92

Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm

Jo, C., Ko, S., Shin, W. C., Han, H., Lee, M. C., Ko, T., & Ro, D. H. (2019) · Knee Surgery Sports Traumatology Arthroscopy · doi:10.1007/s00167-019-05602-3 · Cited by 91

What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?

Kumar, V., Roche, C. P., Overman, S. S., Simovitch, R. W., Flurin, P.-H., Wright, T. W., Zuckerman, J. D., Routman, H. D., & Teredesai, A. M. (2020) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000001263 · Cited by 91

Artificial intelligence in knee arthroplasty: current concept of the available clinical applications

Batailler, C., Shatrov, J. G., Sappey‐Marinier, E., Servıen, E., Parratte, S., & Lustıg, S. (2022) · Arthroplasty · doi:10.1186/s42836-022-00119-6 · Cited by 91

Is Deep Learning On Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid?

Langerhuizen, D. W. G., Bulstra, A. E. J., Janssen, S. J., Ring, D., Kerkhoffs, G. M. M. J., Jaarsma, R. L., & Doornberg, J. N. (2020) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000001318 · Cited by 85

Inflammatory Response in Robotic-Arm-Assisted Versus Conventional Jig-Based TKA and the Correlation with Early Functional Outcomes

Fontalis, A., Kayani, B., Asokan, A. K., Haddad, I. C., Tahmassebi, J. E., Konan, S., Oussedik, S. I., & Haddad, F. S. (2022) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.22.00167 · Cited by 85

Deep Learning Artificial Intelligence Model for Assessment of Hip Dislocation Risk Following Primary Total Hip Arthroplasty From Postoperative Radiographs

Rouzrokh, P., Ramazanian, T., Wyles, C. C., Philbrick, K. A., Cai, J. C., Taunton, M. J., Kremers, H. M., Lewallen, D. G., & Erickson, B. J. (2021) · The Journal of Arthroplasty · doi:10.1016/j.arth.2021.02.028 · Cited by 80

The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty

Karnuta, J. M., Churchill, J. L., Haeberle, H. S., Nwachukwu, B. U., Taylor, S. A., Ricchetti, E. T., & Ramkumar, P. N. (2020) · Journal of Shoulder and Elbow Surgery · doi:10.1016/j.jse.2020.04.009 · Cited by 68

Artificial intelligence for image analysis in total hip and total knee arthroplasty

Gurung, B. B., Liu, P., Harris, P., Sagi, A., Field, R. E., Sochart, D. H., Tucker, K., & Asopa, V. (2022) · The Bone & Joint Journal · doi:10.1302/0301-620x.104b8.bjj-2022-0120.r2 · Cited by 67

Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation

Park, C.-W., Oh, S. J., Kim, K., Jang, M. C., Kim, I. S., Lee, Y.-K., Chung, M. J., Cho, B. H., & Seo, S.-W. (2022) · PLoS ONE · doi:10.1371/journal.pone.0264140 · Cited by 64

Artificial intelligence in orthopedic surgery

Han, X., & Tian, W. (2019) · Chinese Medical Journal · doi:10.1097/cm9.0000000000000479 · Cited by 62

Artificial Intelligence in Sports Medicine: Could GPT-4 Make Human Doctors Obsolete?

Cheng, K., Guo, Q., He, Y., Lu, Y., Xie, R., Li, C., & Wu, H. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03213-1 · Cited by 59

Automated Detection of Periprosthetic Joint Infections and Data Elements Using Natural Language Processing

Fu, S., Wyles, C. C., Osmon, D. R., Carvour, M. L., Sagheb, E., Ramazanian, T., Kremers, W. K., Lewallen, D. G., Berry, D. J., Sohn, S., et al. (2020) · The Journal of Arthroplasty · doi:10.1016/j.arth.2020.07.076 · Cited by 56

Artificial neural network prediction of same-day discharge following primary total knee arthroplasty based on preoperative and intraoperative variables

Wei, C., Quan, T., Wang, K. Y., Gu, A., Fassihi, S. C., Kahlenberg, C. A., Malahias, M., Liu, J., Thakkar, S. C., Valle, A. G. D., et al. (2021) · The Bone & Joint Journal · doi:10.1302/0301-620x.103b8.bjj-2020-1013.r2 · Cited by 55

Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study

Hirvasniemi, J., Gielis, W. P., Arbabı, S., Agricola, R., van Spil, W. E., Arbabi, V., & Weinans, H. (2019) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2019.02.796 · Cited by 52

An increasing number of convolutional neural networks for fracture recognition and classification in orthopaedics

Carmo, L. O. E., van den Merkhof, A., Olczak, J., Gordon, M., Jutte, P. C., Jaarsma, R. L., IJpma, F. F. A., Doornberg, J. N., & Prijs, J. (2021) · Bone & Joint Open · doi:10.1302/2633-1462.210.bjo-2021-0133 · Cited by 52

Using machine learning to predict internal rotation after anatomic and reverse total shoulder arthroplasty

Kumar, V., Schoch, B. S., Allen, C. M. B., Overman, S., Teredesai, A. M., Aibinder, W. R., Parsons, M., Watling, J. P., Ko, J.-W. K., Gobbato, B. B., et al. (2021) · Journal of Shoulder and Elbow Surgery · doi:10.1016/j.jse.2021.10.032 · Cited by 49

Current understanding on artificial intelligence and machine learning in orthopaedics – A scoping review

Kumar, V., Patel, S., Baburaj, V., Vardhan, A., Singh, P. K., & Vaishya, R. (2022) · Journal of Orthopaedics · doi:10.1016/j.jor.2022.08.020 · Cited by 48

Wireless Remote Home Monitoring of Vital Signs in Patients Discharged Early After Esophagectomy: Observational Feasibility Study

Breteler, M. J. M., Numan, L., Ruurda, J. P. H., van Hillegersberg, R., van der Horst, S., Dohmen, D., van Rossum, M., & Kalkman, C. J. (2020) · JMIR Perioperative Medicine · doi:10.2196/21705 · Cited by 47

Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries

Corban, J. P. A. H., Lorange, J.-P., Laverdière, C., Khoury, J., Rachevsky, G., Burman, M. L., & Martineau, P. A. (2021) · Orthopaedic Journal of Sports Medicine · doi:10.1177/23259671211014206 · Cited by 47

Potential benefits, unintended consequences, and future roles of artificial intelligence in orthopaedic surgery research

Kunze, K. N., Orr, M. N., Krebs, V. E., Bhandari, M., & Piuzzi, N. S. (2022) · Bone & Joint Open · doi:10.1302/2633-1462.31.bjo-2021-0123.r1 · Cited by 42

Augmented reality and artificial intelligence-assisted surgical navigation: Technique and cadaveric feasibility study

Siemionow, K., Katchko, K., Lewicki, P., & Luciano, C. J. (2020) · Journal of Craniovertebral Junction and Spine · doi:10.4103/jcvjs.jcvjs_48_20 · Cited by 41

Artificial intelligence in arthroplasty

Purnomo, G., Yeo, S.-J., & Liow, M. H. L. (2021) · Arthroplasty · doi:10.1186/s42836-021-00095-3 · Cited by 39

Artificial intelligence-generated hip radiological measurements are fast and adequate for reliable assessment of hip dysplasia

Archer, H., Reine, S., Alshaikhsalama, A. M., Wells, J. E., Kohli, A., Vazquez, L. C., Hummer, A., DiFranco, M. D., Ljuhar, R., Xi, Y., et al. (2022) · Bone & Joint Open · doi:10.1302/2633-1462.311.bjo-2022-0125.r1 · Cited by 39

Functional implant positioning in total hip arthroplasty and the role of robotic-arm assistance

Fontalis, A., Raj, R. D., Kim, W. J., Gabr, A., Glod, F., Foissey, C., Kayani, B., Putzeys, P., & Haddad, F. S. (2022) · International Orthopaedics · doi:10.1007/s00264-022-05646-0 · Cited by 36

The language of knee alignment

MacDessi, S. J., Oussedik, S. I., Abdel, M. P., Victor, J. M. K., Pagnano, M. W., & Haddad, F. S. (2023) · The Bone & Joint Journal · doi:10.1302/0301-620x.105b2.bjj-2023-00028 · Cited by 36

A novel approach for evaluating bone mineral density of hips based on Sobel gradient-based map of radiographs utilizing convolutional neural network

Nguyen, T. P., Chae, D., Park, S., & Yoon, J. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104298 · Cited by 34

The Use of Artificial Intelligence in Orthopedics: Applications and Limitations of Machine Learning in Diagnosis and Prediction

Innocenti, B., Radyul, Y., & Bori, E. (2022) · Applied Sciences · doi:10.3390/app122110775 · Cited by 34

Concerns surrounding application of artificial intelligence in hip and knee arthroplasty

Polisetty, T. S., Jain, S., Pang, M., Karnuta, J. M., Vigdorchik, J. M., Nawabi, D. H., Wyles, C. C., & Ramkumar, P. N. (2022) · The Bone & Joint Journal · doi:10.1302/0301-620x.104b12.bjj-2022-0922.r1 · Cited by 32

Natural Language Processing and Its Use in Orthopaedic Research

Wyatt, J. M., Booth, G. J., & Goldman, A. H. (2021) · Current Reviews in Musculoskeletal Medicine · doi:10.1007/s12178-021-09734-3 · Cited by 31

Comparison of tibial alignment parameters based on clinically relevant anatomical landmarks

Jang, S. J., Kunze, K. N., Brilliant, Z. R., Henson, M., Mayman, D. J., Jerabek, S. A., Vigdorchik, J. M., & Sculco, P. K. (2022) · Bone & Joint Open · doi:10.1302/2633-1462.310.bjo-2022-0082.r1 · Cited by 30

Not the Last Word: ChatGPT Can’t Perform Orthopaedic Surgery

Bernstein, J. (2023) · Clinical Orthopaedics & Related Research · doi:10.1097/corr.0000000000002619 · Cited by 29

Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study

Alsareii, S. A., Raza, M., Alamri, A. M., AlAsmari, M. Y., Irfan, M., Khan, U. F., & Awais, M. (2022) · Sensors · doi:10.3390/s22041420 · Cited by 29

Interpretation and reporting of predictive or diagnostic machine-learning research in Trauma & Orthopaedics

Farrow, L., Zhong, M., Ashcroft, G. P., Anderson, L., & Meek, R. M. D. (2021) · The Bone & Joint Journal · doi:10.1302/0301-620x.103b12.bjj-2021-0851.r1 · Cited by 28

Osteoporosis: current screening methods, novel techniques, and preoperative assessment of bone mineral density

Al-Hourani, K., Tsang, S.-T. J., & Simpson, A. H. R. W. (2021) · Bone and Joint Research · doi:10.1302/2046-3758.1012.bjr-2021-0452.r1 · Cited by 25

The diagnostic and prognostic value of artificial intelligence and artificial neural networks in spinal surgery

McDonnell, J. M., Evans, S., McCarthy, L., Temperley, H. C., Waters, C., Ahern, D. P., Cunniffe, G. M., Morris, S. F., Synnott, K. A., Birch, N. C., et al. (2021) · The Bone & Joint Journal · doi:10.1302/0301-620x.103b9.bjj-2021-0192.r1 · Cited by 24

Deep learning approach for survival prediction for patients with synovial sarcoma

Han, I., Kim, J. H., Park, H., Kim, H., & Seo, S. W. (2018) · Tumor Biology · doi:10.1177/1010428318799264 · Cited by 22

AI MSK clinical applications: orthopedic implants

Yi, P. H., Mutasa, S., & Fritz, J. M. (2021) · Skeletal Radiology · doi:10.1007/s00256-021-03879-5 · Cited by 20

Artificial intelligence and computer vision in orthopaedic trauma

Prijs, J., Liao, Z., Ashkani‐Esfahani, S., Olczak, J., Gordon, M., Jayakumar, P., Jutte, P. C., Jaarsma, R. L., IJpma, F. F. A., & Doornberg, J. N. (2022) · The Bone & Joint Journal · doi:10.1302/0301-620x.104b8.bjj-2022-0119.r1 · Cited by 20

Development and validation of a predictive model for outcomes in shoulder arthroplasty: a multicenter analysis of nearly 2000 patients

Baessler, A. M., Brolin, T. J., Azar, F. M., Sen, Ś., Chang, M., Falkner, D., Zmistowski, B. M., Routman, H. D., Namdari, S., Gulotta, L. V., et al. (2021) · Journal of Shoulder and Elbow Surgery · doi:10.1016/j.jse.2021.06.007 · Cited by 19

Artificial intelligence and surgical innovation: lower limb arthroplasty

Magan, A. A., Kayani, B., Chang, J. S., Roussot, M. A., Moriarty, P., & Haddad, F. S. (2020) · British Journal of Hospital Medicine · doi:10.12968/hmed.2020.0309 · Cited by 18

Deep learning in orthopaedic research

Vigdorchik, J. M., Jang, S. J., Taunton, M. J., & Haddad, F. S. (2022) · The Bone & Joint Journal · doi:10.1302/0301-620x.104b8.bjj-2022-0416 · Cited by 18

Transsphenoidal surgery using robotics to approach the sella turcica: Integrative use of artificial intelligence, realistic motion tracking and telesurgery

Soldozy, S., Young, S., Yağmurlu, K., Norat, P., Sokolowski, J. D., Park, M. S., Jane, J. A., & Syed, H. R. (2020) · Clinical Neurology and Neurosurgery · doi:10.1016/j.clineuro.2020.106152 · Cited by 16

Author Correction: Automation of surgical skill assessment using a three-stage machine learning algorithm

Lavanchy, J. L., Zindel, J., Kirtac, K., Twick, I., Hosgor, E., Candinas, D., & Beldi, G. (2021) · Scientific Reports · doi:10.1038/s41598-021-88175-x · Cited by 15

Detecting mechanical loosening of total hip replacement implant from plain radiograph using deep convolutional neural networkPreprint

Borjali, A., Chen, A. F., Muratoglu, O. K., Morid, M. A., & Varadarajan, K. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00943 · Cited by 13

A New Approach to Orthopedic Surgery Planning Using Deep Reinforcement Learning and Simulation

Ackermann, J., Wieland, M. J., Hoch, A., Ganz, R., Snedeker, J. G., Oswald, M. R., Pollefeys, M., Zingg, P. O., Esfandiari, H., & Fürnstahl, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87202-1_52 · Cited by 12

Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury

Xie, X., Li, Z., Bai, L., Zhou, R., Li, C., Jiang, X., Zuo, J., & Qi, Y. (2021) · Scientific Programming · doi:10.1155/2021/9935910 · Cited by 12

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

de Bruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., & Essert, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87202-1 · Cited by 12

‘Robosurgeons vs. robosceptics’: can we afford robotic technology or can we afford not to?

Sandhu, J. (2018) · Journal of Clinical Urology · doi:10.1177/2051415818812300 · Cited by 6

Artificial intelligence: revolutionizing robotic surgery: review

Iftikhar, M., Saqib, M., Zareen, M., & Mumtaz, H. (2024) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000002426 · Cited by 161

ChatGPT and large language models in orthopedics: from education and surgery to research

Chatterjee, S., Bhattacharya, M., Pal, S., Lee, S., & Chakraborty, C. (2023) · Journal of Experimental Orthopaedics · doi:10.1186/s40634-023-00700-1 · Cited by 85

Prospective applications of bioactive materials in orthopedic therapies: A review

Liang, W., Zhou, C., Bai, J., Zhang, H., Long, H., Jiang, B., Wang, J., Huang, X., Zhang, H., & Zhao, J. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36152 · Cited by 45

AI in Orthopedic Research: A Comprehensive Review

Mısır, A., & Yuce, A. (2025) · Journal of Orthopaedic Research® · doi:10.1002/jor.26109 · Cited by 46

Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence

Goldman, S. N., Hui, A. T., Choi, S., Mbamalu, E., Tirabady, P., Eleswarapu, A. S., Gómez, J. A., Alvandi, L. M., & Fornari, E. D. (2024) · Spine Deformity · doi:10.1007/s43390-024-00940-w · Cited by 40

An Examination of the Use of Artificial Intelligence in Orthopaedic Surgery

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