Artificial intelligence in orthopaedics: Clinical decision support, medical imaging, surgical planning, and outcome prediction

Table 1 Core artificial intelligence techniques and their orthopaedic clinical applications

AI technique	Description	Orthopaedic application
Machine learning	Algorithms that learn patterns from structured data	Outcome prediction, complication risk analysis
Deep learning	Multilayer neural networks analysing complex datasets	Fracture detection, tumour classification
Computer vision	Automated interpretation of medical images	Imaging diagnosis, surgical navigation
Radiomics	Quantitative extraction of imaging features	Tumour grading, infection differentiation
Natural language processing	Analysis of unstructured clinical text	EMR analysis, research data extraction
Predictive analytics	Statistical modelling for outcome estimation	Implant survivorship prediction
Multimodal AI	Integration of heterogeneous datasets	Precision orthopaedic decision-making
Explainable AI	Transparent model reasoning systems	Clinical trust and regulatory validation

AI: Artificial intelligence; EMR: Electronic medical record.

Table 2 Applications of artificial intelligence across orthopaedic subspecialties

Subspecialty	AI Application	Clinical benefit
Trauma	Fracture detection	Reduced missed injuries
Spine	Surgical planning	Improved alignment accuracy
Arthroplasty	Implant sizing	Enhanced implant longevity
Sports medicine	Injury prediction	Optimised return-to-sport timing
Oncology	Tumour grading	Personalised treatment planning
Infection	Pathogen prediction	Targeted antimicrobial therapy
Paediatric orthopaedics	Growth modelling	Early deformity detection
Rehabilitation	Wearable monitoring	Personalised recovery plans

AI: Artificial intelligence.

Table 3 Advantages and current limitations of artificial intelligence in orthopaedics

Advantages	Limitations
Improved diagnostic accuracy	Dataset bias
Reduced inter-observer variability	Limited external validation
Personalised treatment planning	High infrastructure cost
Faster workflow efficiency	Regulatory uncertainty
Predictive outcome modelling	Algorithm interpretability issues
Early complication detection	Data privacy concerns
Enhanced surgical precision	Learning curve for clinicians
Decision support	Risk of overreliance

Table 4 Summary of artificial intelligence methodologies, validation status, and limitations in orthopaedic applications

Orthopaedic domain	Common AI models	Dataset source	External validation	Clinical readiness	Common limitations
Fracture detection	CNN	Radiographs from hospital databases	Limited	Early clinical use in radiology workflows	Mostly single-centre studies, limited prospective validation
Arthroplasty planning	CNN, machine learning models	Imaging data and arthroplasty registries	Moderate	Integrated with robotic and templating systems	Variability in implant systems and dataset heterogeneity
Spine surgery	CNN, machine learning models	Radiographs, CT, MRI, and clinical records	Limited	Early clinical adoption	Predominantly retrospective datasets
Sports medicine	CNN, deep learning models	MRI datasets	Limited	Early clinical use	Small datasets, variability in imaging protocols
Orthopaedic oncology	Radiomics and CNN	MRI and CT imaging datasets	Limited	Experimental stage	Small sample sizes due to rare tumours
Infection prediction	Machine learning models	Clinical and laboratory datasets	Limited	Early decision-support use	Inconsistent diagnostic criteria and dataset imbalance
Surgical robotics and navigation	Computer vision and AI-assisted navigation	Intraoperative imaging and sensor data	Moderate	Used in robotic arthroplasty and spine surgery	High cost and limited widespread availability
Rehabilitation monitoring	Machine learning and wearable-based AI	Wearable sensor and gait data	Limited	Emerging clinical use	Lack of standardisation and long-term validation

CNN: Convolutional neural networks; AI: Artificial intelligence; CT: Computed tomography; MRI: Magnetic resonance imaging.

Table 5 Emerging technologies shaping the future of artificial intelligence in orthopaedics

Technology	Potential clinical impact
Digital twin modelling	Virtual surgical simulation
Multimodal AI systems	Comprehensive disease prediction
Smart implants	Real-time implant monitoring
Federated learning	Secure multi-institution collaboration
Wearable integration	Continuous rehabilitation tracking
Robotic-AI hybrids	Ultra-precise surgical execution
Generative AI	Automated surgical planning
Continuous-learning models	Adaptive real-time decision support

AI: Artificial intelligence.