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CC BY-NC-ND 4.0 · Revista Chilena de Ortopedia y Traumatología 2025; 66(01): e1-e3
DOI: 10.1055/s-0045-1809059
Editorial

Artificial Intelligence in Orthopedics. Where are We and Where are We Going?

Article in several languages: español | English
Alan Garín 
1   Centro de Cadera Clínica Las Condes, Santiago, Chile
,
Selim Abara
1   Centro de Cadera Clínica Las Condes, Santiago, Chile
,
Javier del Río 
1   Centro de Cadera Clínica Las Condes, Santiago, Chile
,
Joaquín Lara 
1   Centro de Cadera Clínica Las Condes, Santiago, Chile
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Artificial intelligence (AI) has emerged as one of the most promising tools in the transformation of the healthcare sector, offering innovative solutions that support diagnosis, as well as guiding the treatment and rehabilitation of patients.[1] In general terms, AI refers to computational systems capable of performing tasks that usually require human intelligence, such as pattern recognition, learning from data, and decision-making.[2] Within the healthcare field, its impact has been significant in areas such as radiology, robotic surgery, and the optimization of workflows in hospitals.

Current Advances in Artificial Intelligence in Traumatology

In the field of traumatology, AI has begun to play a key supporting role in areas such as diagnostic imaging, predictive analysis of postoperative complications, robotic surgery, and medical training.[1] [2] [3] [4] [5] [Figure 1]

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Fig. 1 Artificial Intelligence in Traumatology.

 

AI in Fracture Detection

Regarding fracture detection, AI algorithms have demonstrated superior diagnostic capability in numerous studies, including the detection of vertebral,[6] [7] humeral,[8] femoral,[9] [10] shoulder,[11] [12] elbow,[13] [14] and ankle fractures,[15] mostly based on X-rays. Both Shen[6] and Zhang,[7] using trained algorithms, demonstrated sensitivity of 83–95%, specificity of 94–98%, and accuracy of 96–97% for the detection of vertebral fractures. Chung[8], by training a convolutional neural network (CNN) on 1,891 X-rays, reported a sensitivity of 99% and a specificity of 97% in the detection of humeral fractures. Beyaz,[9] by training a CNN, demonstrated a sensitivity of 82%, specificity of 72%, and an accuracy of 79% in the detection of femoral fractures. In shoulder cases, Uysal[11] reported a diagnostic accuracy of 84% for fracture detection through the training of two ensemble models. Rayan[13] used a CNN model trained on 58,817 X-rays for detecting elbow fractures in the pediatric population, achieving an accuracy of 88%, sensitivity of 91%, and specificity of 84%. Finally, Ashkani-Esfahani[15] demonstrated a sensitivity of 99%, specificity of 99%, and accuracy of 99% with a Transfer Learning algorithm based on InceptionV3 for the detection of ankle fractures.


 

AI in Postoperative Predictive Analysis

Artificial intelligence can support medical decision-making by interpreting complex analyses as predictors of postoperative complication risk, guiding more personalized clinical management. This analysis takes into account factors associated with each patient (age, sex, associated pathologies, among others), genetic information, and imaging,[16] and can predict patient outcomes. Numerous studies have been published using Machine Learning (ML), which predict the rate of postoperative complications in adults undergoing spine surgery[17] and arthroscopic hip preservation surgery.[18] In knee surgery, studies have been able to predict functional outcomes in patients undergoing osteochondral transplantation,[19] osteoarthritis progression toward prosthetic surgery,[20] and the need for hospitalization after anterior cruciate ligament reconstruction surgery.[21]


 

AI in Robotic Surgery

Robotic surgery leverages the advantages of complex computational calculations to optimize surgical outcomes, achieving greater precision in implant positioning and the intraoperative reduction of fractures in trauma settings. Additionally, the image-based computational planning that underpins robotic surgery allows for more precise bone cuts in the surgical area, optimizing the restoration of limb biomechanics and achieving better soft tissue tension to prevent failure.[22]

The greatest number of cases and demonstrated benefits of robotic surgery have been in lower limb arthroplasty, representing 90% of the market.[22] It has been shown that the use of this technology improves the accuracy of implant positioning in both hip and knee surgeries, although the evidence is less clear regarding functional outcomes or long-term implant survival.[23] [24]

There have also been advances in spinal surgery, ranging from surgical planning to the use of robotic arms with augmented reality.[25] [26]


 

AI in Medical Training

The application of AI in surgical clinical simulators uses immersive reality with automated anatomical visualization, providing users with enhanced surgical experiences and feedback to correct technical errors.[27] The benefits of this training modality have been demonstrated, achieving improved technical precision for both residents and surgeons.[28] [29] Other advantages of using this technology include lower financial costs and reduced radiation exposure compared to training with cadavers.[30]


 

Challenges and Future of AI in Traumatology

While progress is notable, the implementation of AI in clinical practice faces significant challenges. A recent article published by the ISAKOS Young Professionals Task Force[31] showed that only 25% of respondents use AI in clinical practice.

Furthermore, the interpretability of learning algorithms remains an obstacle, as physicians need to understand how predictions are generated to trust them. Furthermore, the integration of these technologies into hospital systems requires adequate infrastructure and staff training. Ethical questions also arise regarding liability in the event of diagnostic or surgical errors resulting from the use of AI.


 

Conclusion

For AI to become a standard support tool in traumatology, it is essential to adopt a proactive approach to its integration. Training in digital technologies and the development of collaborations between physicians, engineers, and data scientists will be key to maximizing the benefits of AI. Additionally, it is crucial to establish ethical regulations and rigorous validation protocols to ensure the safety and effectiveness of these tools in the clinical setting.


 

 

No conflict of interest has been declared by the author(s).


Address for correspondence

Alan Garín, MD
Centro de Cadera Clínica Las Condes
Santiago
Chile   

Publication History

Article published online:
20 May 2025

© 2025. Sociedad Chilena de Ortopedia y Traumatologia. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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Fig. 1 Inteligencia Artificial en Traumatología.
Zoom
Fig. 1 Artificial Intelligence in Traumatology.