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Concept of Artificial Intelligence (AI) and Its Use in Orthopaedic Practice: Applications and Pitfalls: A Narrative Review

DOI: 10.4236/ojo.2024.141004, PP. 32-40

Keywords: Artificial Intelligence, Healthcare, Pitfalls, Drawbacks

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Abstract:

Background: The growth and use of Artificial Intelligence (AI) in the medical field is rapidly rising. AI is exhibiting a practical tool in the healthcare industry in patient care. The objective of this current review is to assess and analyze the use of AI and its use in orthopedic practice, as well as its applications, limitations, and pitfalls. Methods: A review of all relevant databases such as EMBASE, Cochrane Database of Systematic Reviews, MEDLINE, Science Citation Index, Scopus, and Web of Science with keywords of AI, orthopedic surgery, applications, and drawbacks. All related articles on AI and orthopaedic practice were reviewed. A total of 3210 articles were included in the review. Results: The data from 351 studies were analyzed where in orthopedic surgery. AI is being used for diagnostic procedures, radiological diagnosis, models of clinical care, and utilization of hospital and bed resources. AI has also taken a chunk of share in assisted robotic orthopaedic surgery. Conclusions: AI has now become part of the orthopedic practice and will further increase its stake in the healthcare industry. Nonetheless, clinicians should remain aware of AI’s serious limitations and pitfalls and consider the drawbacks and errors in its use.

References

[1]  Meena, T. and Roy, S. (2022) Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift. Diagnostics (Basel), 12, Article No. 2420.
https://doi.org/10.3390/diagnostics12102420
[2]  GBD 2017 Diet Collaborators (2019) Health Effects of Dietary Risks in 195 Countries, 1990-2017: A Systematic Analysis for the Global Burden of Disease Study 2017. The Lancet, 393, 1958-1972.
[3]  Salimi, M., Heidari, M.B., Ravandi, Z., Mosalamiaghili, S., Mirghaderi, P., Jafari Kafiabadi, M., et al. (2023) Investigation of Litigation in Trauma Orthopaedic Surgery. World Journal of Clinical Cases, 11, 1000-1008.
https://doi.org/10.12998/wjcc.v11.i5.1000
[4]  Thabet, A.M., Adams, A., Jeon, S., Pisquiy, J., Gelhert, R., DeCoster, T.A. and Abdelgawad, A. (2022) Malpractice Lawsuits in Orthopedic Trauma Surgery: A Meta-Analysis of the Literature. OTA International, 5, e199.
https://doi.org/10.1097/OI9.0000000000000199
[5]  Deloitte (2018) Deloitte Insights: State of AI in the Enterprise.
https://www2.deloitte.com/content/dam/insights/us/articles/4780_State-of-AI-in-the-enterprise/AICognitiveSurvey2018_Infographic.pdf
[6]  Davenport, T. and Kalakota, R. (2019) The Potential for Artificial Intelligence in Healthcare. Future Healthcare Journal, 6, 94-98.
https://doi.org/10.7861/futurehosp.6-2-94
[7]  Sneha, S. and Raja, J.B. (2019) A Conceptual Overview and Systematic Review of Artificial Intelligence and Its Approaches. International Journal of Emerging Technology and Innovative Engineering, 5, 821-828.
[8]  Vial, A., Stirling, D., Field, M., Ros, M., Ritz, C., Carolan, M., et al. (2018) The Role of Deep Learning and Radiomic Feature Extraction in Cancer-Specific Predictive Modelling: A Review. Translational Cancer Research, 7, 803-816.
https://doi.org/10.21037/tcr.2018.05.02
[9]  Langerhuizen, D.W.G., Janssen, S.J., Mallee, W.H., van den Bekerom, M.P.J., Ring, D., Kerkhoffs, G.M.M.J., et al. (2019) What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review. Clinical Orthopaedics and Related Research®, 477, 2482-2491.
https://doi.org/10.1097/CORR.0000000000000848
[10]  Lindsey, R., Daluiski, A., Chopra, S., Lachapelle, A., Mozer, M., Sicular, S., et al. (2018) Deep Neural Network Improves Fracture Detection by Clinicians. Proceedings of the National Academy of Sciences of the United States of America, 115, 11591-11596.
https://doi.org/10.1073/pnas.1806905115
[11]  Amtmann, D., Kim, J., Chung, H., Bamer, A.M., Askew, R.L., Wu, S., et al. (2014) Comparing CESD-10, PHQ-9, and PROMIS Depression Instruments in Individuals with Multiple Sclerosis. Rehabilitation Psychology, 59, 220-229.
https://doi.org/10.1037/a0035919
[12]  Olczak, J., Fahlberg, N., Maki, A., Razavian, A.S., Jilert, A., Stark, A., et al. (2017) Artificial Intelligence for Analyzing Orthopedic Trauma Radiographs. Acta Orthopaedica, 88, 581-586.
https://doi.org/10.1080/17453674.2017.1344459
[13]  Kim, D.H. and MacKinnon, T. (2018) Artificial Intelligence in Fracture Detection: Transfer Learning from Deep Convolutional Neural Networks. Clinical Radiology, 73, 439-445.
https://doi.org/10.1016/j.crad.2017.11.015
[14]  Oosterhoff, J.H.F. and Doorn-berg, J.N. (2020) Artificial Intelligence in Orthopaedics: False Hope or Not? A Narrative Review along the Line of Gartner’s Hype Cycle. EFORT Open Reviews, 5, 593-603.
https://doi.org/10.1302/2058-5241.5.190092
[15]  Xie, X., Li, Z., Bai, L., Zhou, R., Li, C., Jiang, X., et al. (2021) Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury. Scientific Programming, 2021, Article ID: 9935910.
https://doi.org/10.1155/2021/9935910
[16]  Cheng, K., Guo, Q., He, Y., Lu, Y., Xie, R., Li, C. and Wu, H. (2023) Artificial Intelligence in Sports Medicine: Could GPT-4 Make Human Doctors Obsolete? Annals of Biomedical Engineering, 51, 1658-1662.
https://doi.org/10.1007/s10439-023-03213-1
[17]  Archer, H., Reine, S., Alshaikhsalama, A., Wells, J., Kohli, A., Vazquez, L., Hummer, A., DiFranco, M.D., Ljuhar, R., Xi, Y. and Chhabra, A. (2022) Artificial Intelligence-Generated Hip Radiological Measurements Are Fast and Adequate for Reliable Assessment of Hip Dysplasia: An External Validation Study. Bone & Joint Open, 3, 877-884.
https://doi.org/10.1302/2633-1462.311.BJO-2022-0125.R1
[18]  Farhadi, F., Barnes, M.R., Sugito, H.R., Sin, J.M., Henderson, E.R. and Levy, J.J. (2022) Applications of Artificial Intelligence in Orthopaedic Surgery. Frontiers in Medical Technology, 4, Article ID: 995526.
https://doi.org/10.3389/fmedt.2022.995526
[19]  Nguyen, T.P., Chae, D.S., Park, S.J. and Yoon, J. (2021) A Novel Approach for Evaluating Bone Mineral Density of Hips Based on Sobel Gradient-Based Map of Radiographs Utilizing Convolutional Neural Network. Computers in Biology and Medicine, 132, Article ID: 104298.
https://doi.org/10.1016/j.compbiomed.2021.104298
[20]  Al-Hourani, K., Tsang, S.J. and Simpson, A.H.R.W. (2021) Osteoporosis: Current Screening Methods, Novel Techniques, and Preoperative Assessment of Bone Mineral Density. Bone & Joint Research, 10, 840-843.
https://doi.org/10.1302/2046-3758.1012.BJR-2021-0452.R1
[21]  Jang, S.J., Kunze, K.N., Brilliant, Z.R., Henson, M., Mayman, D.J., Jerabek, S.A., et al. (2022) Comparison of Tibial Alignment Parameters Based on Clinically Relevant Anatomical Landmarks: A Deep Learning Radiological Analysis. Bone & Joint Open, 3, 767-776.
https://doi.org/10.1302/2633-1462.310.BJO-2022-0082.R1
[22]  Gurung, B., Liu, P., Harris, P.D.R., Sagi, A., Field, R.E., Sochart, D.H., et al. (2022) Artificial Intelligence for Image Analysis in Total Hip and Total Knee Arthroplasty: A Scoping Review. The Bone & Joint Journal, 104-B, 929-937.
https://doi.org/10.1302/0301-620X.104B8.BJJ-2022-0120.R2
[23]  Borjali, A., Chen, A.F., Muratoglu, O.K., Morid, M.A. and Varadarajan, K.M. (2020) Detecting Total Hip Replacement Prosthesis Design on Plain Radiographs Using Deep Convolutional Neural Network. Journal of Orthopaedic Research, 38, 1465-1471.
https://doi.org/10.1002/jor.24617
[24]  Schmidt-Erfurth, U., Bogunovic, H., Sadeghipour, A., Schlegl, T., Langs, G., Gerendas, B.S., et al. (2018) Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration. Ophthalmology Retina, 2, 24-30.
https://doi.org/10.1016/j.oret.2017.03.015
[25]  Reed, J.E., Howe, C., Doyle, C. and Bell, D. (2018) Simple Rules for Evidence Translation in Complex Systems: A Qualitative Study. BMC Medicine, 16, Article No. 92.
https://doi.org/10.1186/s12916-018-1076-9
[26]  Ji, S., Gu, Q., Weng, H., Liu, Q., Zhou, P., He, Q., Beyah, R. and Wang, T. (2019) De-Health: All Your Online Health Information Are Belong to Us. 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, 20-24 April 2020, 1609-1620.
https://doi.org/10.1109/ICDE48307.2020.00143
[27]  Baowaly, M.K., Lin, C.C., Liu, C.L. and Chen, K.T. (2019) Synthesizing Electronic Health Records Using Improved Generative Adversarial Networks. Journal of the American Medical Informatics Association, 26, 228-241.
https://doi.org/10.1093/jamia/ocy142
[28]  FDA (2018) FDA Permits Marketing of Artificial Intelligence-Based Device to Detect Certain Diabetes-Related Eye Problems.
https://www.fda.gov
[29]  Lum, Z.C. (2023) Can Artificial Intelligence Pass the American Board of Orthopaedic Surgery Examination? Orthopaedic Residents versus ChatGPT. Clinical Orthopaedics and Related Research®, 481, 1623-1630.
https://doi.org/10.1097/CORR.0000000000002704

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