Transforming orthopedics: A glimpse into the future with artificial intelligence

Image analysis and diagnostics

AI has found extensive application in image analysis and diagnostic capabilities in orthopedics. AI algorithms provide invaluable support to orthopedic professionals and musculoskeletal radiologists. In a recent systematic review of 61 included studies, AI algorithms’ pooled sensitivity and specificity for detecting osteoarthritis were 94% (95% confidence interval [CI]: 90, 97) and 91% (95% CI: 77, 97), respectively.^[1] This not only expedites the diagnostic process but also ensures accurate and earlier interventions. Moreover, the current literature provides evidence of AI’s proficiency in image recognition, classifying knee osteoarthritis with radiographic accuracy comparable to a fellowship-trained arthroplasty surgeon.^[2] AI has also demonstrated excellent reliability in orthopedic fracture detection and classification.^[3] This potential holds significant value, and future strategies should prioritize integrating AI applications into medical record systems, particularly within non-orthopedic settings like primary care physician practices.

AI in pre-operative planning

One of the significant contributions of AI in orthopedics lies in pre-operative planning. Advanced algorithms analyze patient data, including medical history, imaging results, and demographic features (including age, sex, height, weight, and body mass index), to assist surgeons in devising personalized and precise treatment plans.^[4] This not only enhances the efficiency of surgical procedures but also minimizes the risk of complications. This is evident in the reliability of AI in predicting total knee arthroplasty (TKA) component sizes, which exhibited an accuracy ranging from 88.3 to 99.9% ± 1 size.^[4]

Surgical assistance

Integrating AI-powered robotic systems has ushered in a new era of surgical precision. These systems assist surgeons during procedures, offering real-time feedback and enhancing the accuracy of interventions. The application of AI in intraoperative decision-making presents promising avenues, as evidenced by Verstraete et al.,^[5] who successfully harnessed ML models to optimize balance and alignment during TKA surgery.

Post-operative rehabilitation

AI has also been employed in post-operative care following orthopedic procedures. Movement monitoring sensors were found to facilitate continuous tracking of patients’ postoperative range of motion progress, enabling early detection of potential issues and timely interventions that optimize the recovery and rehabilitation process.^[6] In addition, ML holds promise in reducing post-operative opioid requirements and associated complications in total hip arthroplasty by enhancing pre-operative screening and providing support for high-risk patients.^[7]

Challenges and ethical considerations

While the integration of AI in orthopedics holds immense promise, it is not without challenges. Ethical considerations surrounding patient and data privacy, algorithm bias, and decision-making responsibility in AI-assisted surgeries require careful consideration.^[8] For instance, Luo et al.^[9] have undertaken efforts to standardize and formulate guidelines such as the “Guidelines for Developing and Reporting Machine Learning Models in Biomedical Research”. The orthopedic community must actively engage in discussions to establish clear guidelines and frameworks that ensure responsible and ethical use of AI technologies.

AI algorithms may inadvertently reinforce biases in health care due to the quality and diversity of the datasets used for training, which could affect certain patient populations disproportionately. In addition, legal and regulatory issues present significant challenges. One pressing question is: Who bears the liability when AI-guided, robot-assisted surgeries result in poor outcomes, such as malfunctioning arthroplasty? Is the responsibility solely on the surgeon operating the system, or does the technology provider share accountability? The current legal frameworks often place the onus on health-care providers, but as AI systems take on more decision-making roles, the need for updated legal guidelines becomes evident. Future regulations must clarify liability in cases where AI and robotic systems contribute to errors, as suggested by Luo et al.^[9] Clear protocols regarding oversight and responsibility will be critical as AI continues to shape surgical practice.

Impact on the training of future surgeons

The increasing reliance on AI and robotic technologies in surgery raises concerns about the potential deskilling of surgeons in traditional techniques. As AI systems take on more diagnostic and intraoperative roles, there is a risk that future surgeons may become overly dependent on these tools, potentially undermining their manual dexterity and decision-making abilities in non-AI-assisted environments.^[10] In scenarios such as natural disasters, humanitarian crises, or information technology disruptions caused by hackers, where AI technologies may be unavailable, surgeons could struggle to perform complex procedures without technological aid.

To mitigate this risk, surgical training programs must emphasize a dual approach: while embracing AI for its benefits, they should also ensure that surgeons maintain core competencies in traditional methods, fostering resilience in both high-tech and low-resource settings.^[10,11] This balanced training approach is crucial for developing well-rounded surgeons capable of adapting to diverse and unpredictable environments.