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.