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

Table 1 Comparison of conventional vs artificial intelligence/machine learning enhanced spine care

Aspect	Conventional approach	AI/ML-enhanced approach	Advantages of AI/ML	Ref.
Diagnosis of spinal disorders	Manual interpretation of MRI/CT scans by radiologists	Automated image analysis using deep learning (e.g., CNNs) to detect abnormalities	Faster, more accurate, and consistent diagnosis with reduced subjectivity	[20]
Scoliosis detection	Manual measurement of Cobb angles from X-rays	AI algorithms for automated Cobb angle measurement and severity classification	Reduced time, improved accuracy, and early detection	[16]
Surgical planning	Generic surgical plans based on population data and surgeon experience	AI-driven predictive models for personalized surgical planning and outcome prediction	Improved precision, reduced complications, and better patient outcomes	[21]
Intraoperative navigation	Manual guidance using fluoroscopy and surgeon expertise	AI-powered robotic systems and AR for real-time navigation	Enhanced precision, reduced radiation exposure, and fewer surgical errors	[22]
Post-operative monitoring	In-person follow-ups and subjective patient feedback	Wearable AI devices and remote monitoring systems for real-time tracking of recovery	Continuous monitoring, improved adherence, and early detection of complications	[23]
Rehabilitation	Standardized physiotherapy protocols	AI-powered virtual physiotherapy and personalized exercise recommendations	Tailored rehabilitation, increased accessibility, and cost-effectiveness	[24]
Pain management	Generalized pain management protocols	AI models predicting pain progression and recommending personalized interventions	Improved pain control and patient satisfaction	[25]
Spinal tumor classification	Manual classification of tumors from imaging data	Deep learning models for automated tumor classification and grading	Faster and more accurate diagnosis, improved treatment planning	[26]
Degenerative disease prediction	Reliance on patient history and imaging without predictive analytics	ML models predicting the progression of degenerative spine diseases	Early intervention and reduced disease severity	[27]
Implant design	Standardized implants based on average patient anatomy	AI-driven generative design for patient-specific implants	Better fit, reduced complications, and improved outcomes	[28]
Telemedicine	Limited to in-person consultations	AI-powered telemedicine platforms for remote diagnosis and consultation	Increased access to care, especially in underserved areas	[29]
Surgical complication prediction	Surgeon intuition and experience-based risk assessment	ML models predicting risks of infection, blood loss, or implant failure	Reduced surgical risks and improved patient safety	[30]
Radiation dose optimization	Fixed imaging protocols with high radiation exposure	AI-enhanced imaging protocols reducing radiation dose while maintaining image quality	Safer imaging with reduced radiation exposure	[31]
Biomechanical analysis	Manual analysis of spinal movement patterns	AI models analyzing spinal biomechanics for surgical and rehabilitation planning	Enhanced precision and personalized care	[14]
Patient adherence tracking	Reliance on patient self-reporting and manual documentation	AI-powered wearable devices and apps tracking adherence to rehabilitation protocols	Improved patient compliance and outcomes	[32]

AI: Artificial intelligence; ML: Machine learning; MRI: Magnetic resonance imaging; CT: Computed tomography; CNNs: Convolutional neural networks; AR: Augmented reality.

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.