NEW HEIGHTS IN ORTHOPEDIC MEDICINE: GLOBAL IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING
Keywords:
Artificial Intelligence, Deep Learning, Orthopedics, Orthopedic Surgery, Orthopedic Radiography, Medical Imaging Practice, AIAbstract
As Artificial Intelligence (AI) becomes more popular, its applications in medicine, particularly orthopedic therapy, are to be explored to improve the diagnosis process. The implementation of AI’s benefits extends to a broad audience, including patients and medical professionals. Firstly, AI contributes to the diagnostic capabilities of medical imaging by significantly decreasing human error and intra and inter-observability. Additionally, AI optimizes workflow for the medical professional, mainly by automating time-staking processes to leave more time for critical analyses and patient-physician interaction. For the patient, AI can also decrease the health costs associated with medical imaging. Lastly, the fears surrounding AI replacing medical professionals are countered, saying that the application-specific nature and database limitations lend themselves to high dependency on medical professionals. In conclusion, AI should be implemented globally as a tool to improve patient care and efficiency in the medical field. This paper was written to synthesize current material on AI’s benefits and limitations, ultimately to counter stigmas surrounding AI and spur further research on the topic to get closer to global implementation.
References
I. Ashkani-Esfahani, S. (2021, January 4). Artificial Intelligence Improves Orthopedic Diagnosis. Massachusetts General Hospital. Retrieved October 6, 2023, from https://advances.massgeneral.org/ortho/article.aspx?id=1330
II. Chen, K., Stotter, C., Klestil, T., & Nehrer, S. (2022). Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review. Diagnostics, 12(9), 2235.
III. Hill, B. G., Krogue, J. D., Jevsevar, D. S., & Schilling, P. L. (2022). Deep learning and imaging for the orthopaedic surgeon: how machines “read” radiographs. JBJS, 104(18), 1675-1686.
IV. Kimia Lab. (2020, November 11). Artificial intelligence in medical imaging [Video]. YouTube. https://www.youtube.com/watch?v=xwgr5QuL_Ig
V. Kurmis, A. P., & Ianunzio, J. R. (2022). Artificial intelligence in orthopedic surgery: evolution, current state and future directions. Arthroplasty, 4(1), 1-10.
VI. Maffulli, N., Rodriguez, H. C., Stone, I. W., Nam, A., Song, A., Gupta, M., ... & Gupta, A. (2020). Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol. Journal of orthopaedic surgery and research, 15, 1-5.
VII. Potočnik, J., Foley, S., & Thomas, E. (2023). Current and potential applications of artificial intelligence in medical imaging practice: A narrative review. Journal of Medical Imaging and Radiation Sciences.
VIII. Rizi-Shorvon, E. (2023, June 27). How AI is helping to shrink waiting times for NHS cancer patients. Microsoft News Centre UK. https://news.microsoft.com/en-gb/2023/06/27/ai-helping-shrink-waiting-times-nhs-cancer-patients/
IX. van Leeuwen, K. G., de Rooij, M., Schalekamp, S., van Ginneken, B., & Rutten, M. J. (2021). How does artificial intelligence in radiology improve efficiency and health outcomes?. Pediatric Radiology, 1-7.
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