Role of AI in Orthopaedic Surgery: Current Use, Challenges and Future

Author: Dr Suhail Chughtai, FRCS, FFLM

Artificial intelligence (AI) is transforming orthopaedic surgery by enhancing diagnostic accuracy, improving surgical outcomes, and streamlining healthcare processes. This article explores AI's applications in orthopaedics, its deployment methodology, challenges, case studies, and the future landscape.

CURRENT APPLICATIONS

Diagnostic Support

AI excels in interpreting complex imaging modalities. For instance, convolutional neural networks (CNNs) are used to detect fractures, grade arthritis, and identify spinal pathologies with precision. AI also supports preoperative planning by analyzing imaging data to create detailed anatomical models (BOA Homepage / PLOS)

Surgical Assistance

Robotic systems like MAKO and ROSA leverage AI for precision in procedures such as knee arthroplasty and spine surgery. These systems enable image-guided navigation, haptic feedback, and enhanced alignment during surgeries​ (BOA Homepage)

Outcome Prediction

AI models predict patient-specific outcomes, aiding personalised care. For example, machine learning algorithms analyze large datasets to forecast post-surgical complications, recovery times, and implant success (PLOS)

DEPLOYMENT METHODOLOGY

Data Collection and Annotation

AI relies on large datasets from electronic health records (EHRs), imaging systems, and surgical outcomes. High-quality, annotated data is essential to train and validate models​ (JEO Esska / ​PLOS)

Model Development

Techniques such as machine learning and deep learning are employed. Algorithms like random forests and support vector machines are common, with increasing use of neural networks​ (PLOS)

Integration into Clinical Workflow

AI tools are embedded into imaging systems, decision-support platforms, or robotic systems, ensuring seamless use alongside human expertise​ (JEO Esska)

CHALLENGES

Data Quality and Bias

AI's reliability depends on diverse and high-quality datasets. Bias in training data can lead to unequal outcomes across populations (JEO Esska)

Regulatory and Ethical Issues

Regulations such as the European AI Act emphasise transparency, human oversight, and data security. Compliance with these standards is resource-intensive​ (JEO Esska)

Adoption Barriers. Clinicians face a steep learning curve with new technologies, and integrating AI into established workflows often requires significant investment (PLOS).

CASE STUDIES

AI in Fracture Detection

A study demonstrated that AI outperformed radiologists in identifying wrist fractures on X-rays, reducing diagnostic errors and expediting treatment (JEO Esska)

Robotics in Arthroplasty

The MAKO robotic system improved implant positioning accuracy in knee replacement surgeries, leading to better long-term outcomes compared to traditional methods​ (BOA Homepage)

FUTURE VISION

Digital Twins

Digital twin technology is emerging, creating patient-specific virtual models to simulate surgical outcomes and personalize interventions (JEO Esska)

AI-Driven Predictive Analytics

Future tools may combine AI with wearable sensors and real-time monitoring to predict musculoskeletal injuries and optimize rehabilitation plans (JEO Esska)

Collaborative Systems

AI will likely evolve into a collaborative partner, complementing rather than replacing human surgeons, enhancing decision-making, and enabling more minimally invasive procedures​ (BOA Homepage/ PLOS)

CONCLUSION

AI in orthopaedics is revolutionising patient care by enhancing diagnostic precision, surgical accuracy, and personalised treatments. Despite challenges in data quality, regulation, and adoption, advancements like digital twins and predictive analytics hold immense potential. Continued collaboration between technologists, clinicians, and policymakers will ensure AI's ethical and effective integration into orthopaedic practice.

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