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

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.