Artificial intelligence and machine learning in spine care: Advancing precision diagnosis, treatment, and rehabilitation

Table 2 Artificial intelligence/machine learning applications in spine care: Diagnosis, treatment, and rehabilitation

Application area	AI/ML technique used	Example use case	Clinical benefits	Challenges	Ref.
Automated spinal image analysis	Deep learning (CNN, RNN)	MRI/CT segmentation for detecting vertebral fractures, herniated discs, and stenosis	Increased diagnostic precision, reduced human error	Data scarcity, model generalization issues	[45]
Predictive analytics for spinal degeneration	Machine learning (random forest, SVM)	Identifying early signs of degenerative disc disease using patient history and imaging	Early intervention, reduced disease progression	Need for longitudinal datasets	[46]
AI-guided scoliosis detection	Deep learning (CNN)	Automated scoliosis classification from spinal X-rays	Faster screening, improved sensitivity	Variability in X-ray quality	[47]
AI-assisted surgical planning	Reinforcement learning, predictive analytics	ML models optimizing screw placement and implant selection for spinal fusion	Reduced complications, better surgical outcomes	Lack of real-world validation	[48]
Robotic-assisted spine surgery	AI-powered robotics	AI-guided navigation in minimally invasive spinal surgeries	Higher precision, reduced recovery time	High costs, regulatory approval challenges	[49]
AI-driven post-operative monitoring	Wearable AI and IoT	Continuous tracking of patient mobility and spinal alignment post-surgery	Improved rehabilitation adherence	Data privacy and security risks	[22]
ML for spinal trauma prognosis	Supervised learning (SVM, decision trees)	Predicting recovery outcomes in spinal cord injuries	Personalized treatment plans	Need for real-world validation	[50]
AI-based virtual physiotherapy	NLP and AI chatbots	AI-powered virtual physiotherapy apps for home rehabilitation	Increased accessibility, cost reduction	Limited personalization	[24]
Deep learning for tumor classification	CNN-based image recognition	Automated classification of spinal tumors from MRI scans	Faster diagnosis, improved treatment planning	Need for diverse datasets	[51]
AI-enabled pain management	ML-based pain prediction models	Predicting chronic pain progression using patient-reported data and imaging	Personalized pain management	Variability in pain perception	[52]
AI in spinal deformity progression prediction	ML-based predictive modeling	Forecasting scoliosis progression based on patient history	Early intervention, reduced severity	Need for larger datasets	[53]
Multimodal AI for spine care	Integration of imaging, genetics, and clinical data	Personalized spinal disease prediction using multimodal AI	Holistic patient assessment	Computational complexity	[54]
AI for radiation dose optimization	AI-enhanced imaging protocols	Reducing radiation exposure during spinal X-rays and CT scans	Safer imaging techniques	Balancing image quality with low radiation	[15]
NLP in spine care	AI-powered NLP models	Extracting spine-related clinical insights from medical records	Improved documentation, faster research	Need for domain-specific NLP models	[55]
AI-enhanced spinal biomechanics analysis	ML for motion prediction	Studying spinal movement patterns for biomechanical modeling	Enhanced surgical and rehab planning	Complexity of real-world biomechanics	[56]
AI in AR for spine surgery	AR + AI integration	Real-time AR overlays for spinal anatomy during surgery	Enhanced precision, reduced surgical errors	High costs, technical complexity	[57]

AI: Artificial intelligence; ML: Machine learning; MRI: Magnetic resonance imaging; CT: Computed tomography; CNN: Convolutional neural network; RNN: Recurrent neural network; SVM: Support vector machine; NLP: Natural language processing; AR: Augmented reality.