Artificial Intelligence for the Orthopaedic Surgeon: An Overview of Potential Benefits, Limitations, and Clinical Applications

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works

Dissecting racial bias in an algorithm used to manage the health of populations

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · see citing works

Health Care Spending in the United States and Other High-Income Countries

Papanicolas, I. N., Woskie, L., & Jha, A. K. (2018) · JAMA · doi:10.1001/jama.2018.1150 · Cited by 1,628 · see citing works

Introduction to artificial intelligence in medicine

Mintz, Y., & Brodie, R. (2019) · Minimally Invasive Therapy & Allied Technologies · doi:10.1080/13645706.2019.1575882 · Cited by 818 · see citing works

Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions

Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., Spitzer, A. I., & Ramkumar, P. N. (2020) · Current Reviews in Musculoskeletal Medicine · doi:10.1007/s12178-020-09600-8 · Cited by 718 · see citing works

Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists

Jha, S., & Topol, E. J. (2016) · JAMA · doi:10.1001/jama.2016.17438 · Cited by 622 · see citing works

Recent Increases in the U.S. Maternal Mortality Rate: Disentangling Trends From Measurement Issues

MacDorman, M. F., Declercq, E., Cabral, H., & Morton, C. (2016) · Obstetrics & Gynecology · doi:10.1097/aog.0000000000001556 · Cited by 487 · see citing works

On the Prospects for a (Deep) Learning Health Care System

Naylor, C. D. (2018) · JAMA · doi:10.1001/jama.2018.11103 · Cited by 250 · see citing works

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 243 · see citing works

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 · see citing works

Measure once, cut twice—adding patient-reported outcome measures to the electronic health record for comparative effectiveness research

Wu, A. W., Kharrazi, H. H. K., Boulware, L. E., & Snyder, C. F. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2013.04.005 · Cited by 187 · see citing works

Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model

Navarro, S. M., Wang, E. Y., Haeberle, H. S., Mont, M. A., Krebs, V. E., Patterson, B. M., & Ramkumar, P. N. (2018) · The Journal of Arthroplasty · doi:10.1016/j.arth.2018.08.028 · Cited by 157 · see citing works

Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review

Haeberle, H. S., Helm, J. M., Navarro, S. M., Karnuta, J. M., Schaffer, J. L., Callaghan, J. J., Mont, M. A., Kamath, A. F., Krebs, V. E., & Ramkumar, P. N. (2019) · The Journal of Arthroplasty · doi:10.1016/j.arth.2019.05.055 · Cited by 146 · see citing works

Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models

Ramkumar, P. N., Navarro, S. M., Haeberle, H. S., Karnuta, J. M., Mont, M. A., Iannotti, J. P., Patterson, B. M., & Krebs, V. E. (2018) · The Journal of Arthroplasty · doi:10.1016/j.arth.2018.12.030 · Cited by 143 · see citing works

Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty

Karhade, A. V., Schwab, J. H., & Bedair, H. S. (2019) · The Journal of Arthroplasty · doi:10.1016/j.arth.2019.06.013 · Cited by 130 · see citing works

Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model

Ramkumar, P. N., Karnuta, J. M., Navarro, S. M., Haeberle, H. S., Scuderi, G. R., Mont, M. A., Krebs, V. E., & Patterson, B. M. (2019) · The Journal of Arthroplasty · doi:10.1016/j.arth.2019.05.034 · Cited by 111 · see citing works

Detecting knee osteoarthritis and its discriminating parameters using random forests

Kotti, M., Duffell, L. D., Faisal, A. A., & McGregor, A. H. (2017) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2017.02.004 · Cited by 107 · see citing works

Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring

Ramkumar, P. N., Haeberle, H. S., Bloomfield, M. R., Schaffer, J. L., Kamath, A. F., Patterson, B. M., & Krebs, V. E. (2019) · The Journal of Arthroplasty · doi:10.1016/j.arth.2019.06.018 · Cited by 104 · see citing works

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip

Karnuta, J. M., Haeberle, H. S., Luu, B. C., et al. (2020) · The Journal of Arthroplasty · doi:10.1016/j.arth.2020.11.015 · Cited by 104 · see citing works

Optimisation of orthopaedic implant design using statistical shape space analysis based on level sets

Kozic, N., Weber, S., Büchler, P., et al. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.02.008 · Cited by 87 · see citing works

Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model

Ramkumar, P. N., Karnuta, J. M., Navarro, S. M., Haeberle, H. S., Iorio, R. R., Mont, M. A., Patterson, B. M., & Krebs, V. E. (2019) · The Journal of Arthroplasty · doi:10.1016/j.arth.2019.04.055 · Cited by 87 · see citing works

Extracting and utilizing electronic health data from Epic for research

Milinovich, A., & Kattan, M. W. (2018) · Annals of Translational Medicine · doi:10.21037/atm.2018.01.13 · Cited by 82 · see citing works

Risk Adjustment Is Necessary in Medicare Bundled Payment Models for Total Hip and Knee Arthroplasty

Courtney, P. M., Bohl, D. D., Lau, E., et al. (2018) · The Journal of Arthroplasty · doi:10.1016/j.arth.2018.02.095 · Cited by 73 · see citing works

Risk Prediction Tools for Hip and Knee Arthroplasty

Manning, D. W., Edelstein, A. I., & Alvi, H. M. (2016) · Journal of the American Academy of Orthopaedic Surgeons · doi:10.5435/jaaos-d-15-00072 · Cited by 65 · see citing works

Advanced decision‐making using patient‐reported outcome measures in total joint replacement

Jayakumar, P., & Bozic, K. J. (2020) · Journal of Orthopaedic Research® · doi:10.1002/jor.24614 · Cited by 62 · see citing works

The Ethics of Bundled Payments in Total Joint Replacement: “Cherry Picking” and “Lemon Dropping”

Humbyrd, C. J. (2018) · The Journal of Clinical Ethics · doi:10.1086/jce2018291062 · Cited by 60 · see citing works

Why are patients dissatisfied after spine surgery when improvements in disability and pain are clinically meaningful?

Sivaganesan, A., Khan, I., Pennings, J. S., Roth, S. G., Nolan, E., Oleisky, E. R., Asher, A. L., Bydon, M., Devin, C. J., & Archer, K. R. (2020) · The Spine Journal · doi:10.1016/j.spinee.2020.06.008 · Cited by 60 · see citing works

A Novel Risk Calculator Predicts 90-Day Readmission Following Total Joint Arthroplasty

Goltz, D. E., Ryan, S. P., Hopkins, T. J., Howell, C. B., Attarian, D. E., Bolognesi, M. P., & Seyler, T. M. (2019) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.18.00843 · Cited by 52 · see citing works

Who Goes to Inpatient Rehabilitation or Skilled Nursing Facilities Unexpectedly Following Total Knee Arthroplasty?

Rondon, A. J., Tan, T. L., Greenky, M. R., et al. (2017) · The Journal of Arthroplasty · doi:10.1016/j.arth.2017.12.015 · Cited by 50 · see citing works

Can a machine learning model accurately predict patient resource utilization following lumbar spinal fusion?

Karnuta, J. M., Golubovsky, J. L., Haeberle, H. S., Rajan, P. V., Navarro, S. M., Kamath, A. F., Schaffer, J. L., Krebs, V. E., Pelle, D. W., & Ramkumar, P. N. (2019) · The Spine Journal · doi:10.1016/j.spinee.2019.10.007 · Cited by 41 · see citing works

Risk Adjustment for Medicare Total Knee Arthroplasty Bundled Payments

Clement, R. C., Derman, P. B., Kheir, M. M., et al. (2016) · Orthopedics · doi:10.3928/01477447-20160623-04 · Cited by 32 · see citing works

Characteristics of Hospitals Earning Savings in the First Year of Mandatory Bundled Payment for Hip and Knee Surgery

Navathe, A. S., Liao, J. M., Shah, Y., et al. (2018) · JAMA · doi:10.1001/jama.2018.0678 · Cited by 31 · see citing works

The Post-Physician Era: Medicine in the 21st Century

Anderson, P. C. (1977) · JAMA · doi:10.1001/jama.1977.03270480076033 · Cited by 26 · see citing works

How Accurately Are We Coding Readmission Diagnoses After Total Joint Arthroplasty?

Saucedo, J. M., Marecek, G. S., Lee, J. J., et al. (2013) · The Journal of Arthroplasty · doi:10.1016/j.arth.2013.05.006 · Cited by 25 · see citing works

How to Succeed in Bundled Payments for Total Joint Replacement

Mouille, B., Higuera, C. A., Woicehovich, L., et al. (2016) · NEJM Catalyst · Cited by 9 · see citing works

Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond

Nasir, S., Khan, R. A., & Bai, S. (2024) · IEEE Access · doi:10.1109/access.2024.3369912 · Cited by 152 · see citing works

DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION

Mustafa, N. H., Kelesbayev, D., & Alibekova, Z. D. (2018) · The Journal of Economic Research & Business Administration · doi:10.26577/be-2018-2-2141

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Genentech taps Affimed’s antibodies

Cross, R. (2018) · C&EN Global Enterprise · doi:10.1021/cen-09635-buscon15

KOMPLIKASI REGIONAL ANESTESI PADA PEDIATRIK

Sari, D. (2023) · Jurnal Komplikasi Anestesi · doi:10.22146/jka.v5i1.7322

Enxerto livre de arteria epigastrica inferior: avaliacao hemodinamica e angiografica pre e pos-operatoria

Dib, A., Puig, L. B., Salvadori, D., et al. (1988) · Arquivos Brasileiros de Cardiologia