Orthopedic surgeons’ attitudes and expectations toward artificial intelligence: A national survey study
Artificial intelligence and orthopedic surgeons
Keywords:artificial intelligence, attitudes, orthopedic surgeons, survey
Background/Aim: There is a lack of understanding of artificial intelligence (AI) among orthopedic surgeons regarding how it can be used in their clinical practices. This study aimed to evaluate the attitudes of orthopedic surgeons regarding the application of AI in their practices.
Methods: A cross-sectional study was conducted in Turkey among 189 orthopedic surgeons between November 2021 and February 2022. An electronic survey was designed using the SurveyMonkey platform. The questionnaire included six subsections related to AI usefulness in clinical practice and participants’ knowledge about the topic. It also surveyed their acceptance level of learning, concerns about the potential risks of AI, and implementation of this technology into their daily practice
Results: A total of 33.9% of the participants indicated that they were familiar with the concept of AI, while 82.5% planned to learn about artificial intelligence in the coming years. Most of the surgeons (68.3%) reported not using AI in their daily practice. The activities of orthopedic associations focused on AI were insufficient according to 77.2% of participants. Orthopedic surgeons expressed concern over AI involvement in the future regarding an insensitive and nonempathic attitude toward the patient (53.5%). A majority of respondents (80.4%) indicated that AI was most feasible in extremity reconstruction. Pelvis fractures were found in the region where the AI system is most needed in the fracture classification (68.7%).
Conclusion: Most of the respondents did not use AI in their daily clinical practice; however, almost all surgeons had plans to learn about artificial intelligence in the future. There was a need to improve orthopedic associations’ activities focusing on artificial intelligence. Furthermore, new research including the medical ethics issues of the field will be needed to allay the surgeons’ worries. The classification system of pelvic fractures and sub-branches of orthopedic extremity reconstruction were the most feasible areas for AI systems. We believe that this study will serve as a guide for all branches of orthopedic medicine.
Cabitza F, Locoro A, Banfi G. Machine learning in orthopedics: A literature review. Front Bioeng Biotechnol. 2018;6:75. DOI: https://doi.org/10.3389/fbioe.2018.00075
Choy G, Khalilzadeh O, Michalski M, Do S, Samir AE, Piankh OS, et al. Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318–28. DOI: https://doi.org/10.1148/radiol.2018171820
Panchmatia JR, Visenio MR, Panch T. The role of artificial intelligence in orthopedic surgery. Br J Hosp Med. 2018;79(12):676–81. DOI: https://doi.org/10.12968/hmed.2018.79.12.676
Federer SJ, Jones GG. Artificial intelligence in orthopedics: A scoping review. PLoS ONE. 2021;16(11):e0260471. doi: 10.1371/journal.pone.0260471.
Urakawa T, Tanaka Y, Goto S, Matsuzawa H, Watanabe K, Endo N. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skelet Radiol. 2019;48:239–44. DOI: https://doi.org/10.1007/s00256-018-3016-3
Yamada Y, Maki S, Kishida S, Nagai H, Arima J, Yamakawa N, et al. Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs. Acta Orthop. 2020;91:699–704. DOI: https://doi.org/10.1080/17453674.2020.1803664
Choi JW, Cho YJ, Lee S, Lee J, Lee S, Choi YH, et al. Using a dual-input convolutional neural network for automated detection of pediatric supracondylar fracture on conventional radiography. Investig Radiol. 2020;55:101–10. DOI: https://doi.org/10.1097/RLI.0000000000000615
Rouzrokh P, Wyles CC, Philbrick KA, Ramazanian T, Weston AD, Cai JC, et al. A deep learning tool for automated radiographic measurement of acetabular component inclination and version after total hip arthroplasty. J Arthroplasty. 2021;36(7):2510–7.e6. doi: 10.1016/j.arth.2021.02.026 DOI: https://doi.org/10.1016/j.arth.2021.02.026
Schock J, Truhn D, Abrar DB, Merhof D, Conrad S, Post M, et al. Automated analysis of alignment in long-leg radiographs by using a fully automated support system based on artificial intelligence. Radiol Artif Intell. 2021;3(2):e200198. DOI: https://doi.org/10.1148/ryai.2020200198
Lambrechts A, Ganapathi M, Wirix-Speetjens R. Clinical Evaluation of Artificial Intelligence-based Preoperative Plans for Total Knee Arthroplasty. CAOS 2020 - The 20th Annual Meeting of the International Society for Computer Assisted Orthopedic Surgery: EasyChair; 2020;169–73.
Li Z, Zhang X, Ding L, Du K, Yan J, Chan MTV, et al. Deep learning approach for guiding three-dimensional computed tomography reconstruction of lower limbs for robotically-assisted total knee arthroplasty. Int J Med Robot. 2021;17(5):e2300. doi: 10.1002/rcs.2300. DOI: https://doi.org/10.1002/rcs.2300
Jacofsky DJ, Allen M. Robotics in Arthroplasty: A Comprehensive Review. J Arthroplasty. 2016;31(10):2353–63. doi: 10.1016/j.arth.2016.05.026 DOI: https://doi.org/10.1016/j.arth.2016.05.026
Oh S, Kim JH, Choi SW, Lee HJ, Hong J, Kwon SH. Physician Confidence in Artificial Intelligence: An Online Mobile Survey J Med Internet Res. 2019;21(3):e12422. DOI: https://doi.org/10.2196/12422
Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. 2018;23;6(11):e12106. doi: 10.2196/12106 DOI: https://doi.org/10.2196/12106
Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med. 2018;48:e13-4. doi: 10.1016/j.ejim.2017.06.017 DOI: https://doi.org/10.1016/j.ejim.2017.06.017
Sarwar S, Dent A, Faust K, Richer M, Djuric U, Ommeren RV, et al. Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPJ Digit Med. 2019;2:28. doi: 10.1038/s41746-019-0106-0 DOI: https://doi.org/10.1038/s41746-019-0106-0
Paley D, Herzenberg JE, Tetsworth K, McKie J, Bhave A. Deformity planning for frontal and sagittal plane corrective osteotomies. Orthop Clin North Am. 1994;25(3):425-65. DOI: https://doi.org/10.1016/S0030-5898(20)31927-1
Tsafnat G, Glasziou P, Choong MK, Dunn A, Galgani F, Coiera E. Systematic review automation technologies. Systematic Reviews. 2014;3:1–15. DOI: https://doi.org/10.1186/2046-4053-3-74
Federer SJ, Jones GG. Artificial intelligence in orthopedics: A scoping review. PLoS ONE. 2021;16(11): e0260471. doi: 10.1371/journal.pone.0260471 DOI: https://doi.org/10.1371/journal.pone.0260471
Kim DH, MacKinnon T. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol. 2018;73(5):439–45. doi: 10.1016/j.crad.2017.11.015 DOI: https://doi.org/10.1016/j.crad.2017.11.015
Olczak J, Fahlberg N, Maki A, Razavian AS, Jilert A, Stark A, et al. Artificial intelligence for analyzing orthopedic trauma radiographs Deep learning algorithms-are they on par with humans for diagnosing fractures? Acta Orthopedica. 2017;88(6):581–6. doi: 10.1080/17453674.2017.1344459 DOI: https://doi.org/10.1080/17453674.2017.1344459
Chung SW, Han SS, Lee JW, Oh KS, 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. doi: 10.1080/17453674.2018.1453714 DOI: https://doi.org/10.1080/17453674.2018.1453714
Gan KF, Xu DL, Lin YM, Shen Y, Zhang T, Hu K, et al. Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthopedica. 2019;90(4):394–400. doi: 10.1080/17453674.2019.1600125 DOI: https://doi.org/10.1080/17453674.2019.1600125
Langerhuizen DWG, Janssen SJ, Mallee WH, Van Den Bekerom MPJ, Ring D, Karkhoffs GMMJ, et al. What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopedic Trauma Imaging? A Systematic Review. Clin Orthop Relat Res. 2019; 477(11):2482-91. doi: 10.1097/CORR.0000000000000848 DOI: https://doi.org/10.1097/CORR.0000000000000848
Liang S, Tang F, Huang X, Yang K, Zhong T, Hu R, et al. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning. Eur Radiol. 2019;29(4):1961–7. doi: 10.1007/s00330-018-5748-9 DOI: https://doi.org/10.1007/s00330-018-5748-9
Wong K, Gallant F, Szumacher E. Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology–national survey. J Med Imaging Radiat Sci. 2021;52(1):44–8. doi: 10.1016/j.jmir.2020.11.013 DOI: https://doi.org/10.1016/j.jmir.2020.11.013
Abdullah R, Fakieh B. Health Care Employees' Perceptions of the Use of Artificial Intelligence Applications: Survey Study. J Med Internet Res. 2020;22(5):e17620. doi: 10.2196/17620 DOI: https://doi.org/10.2196/17620
Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, et al. Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging. 2020;11:14. doi: 10.1186/s13244-019-0830-7 DOI: https://doi.org/10.1186/s13244-019-0830-7
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