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

Abstract

Artificial intelligence (AI) and machine learning (ML) are transforming spine care by addressing diagnostics, treatment planning, and rehabilitation challenges. This study highlights advancements in precision medicine for spinal pathologies, leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms, enabling faster and more accurate detection of abnormalities. AI-powered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries. Wearable devices and virtual platforms, designed with AI, offer personalized, adaptive therapies that improve treatment adherence and recovery outcomes. AI also enables preventive interventions by assessing spine condition risks early. Despite progress, challenges remain, including limited healthcare datasets, algorithmic biases, ethical concerns, and integration into existing systems. Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care. Future developments include multimodal AI systems integrating imaging, clinical, and genetic data for holistic treatment approaches. AI and ML promise significant improvements in diagnostic accuracy, treatment personalization, service accessibility, and cost efficiency, paving the way for more streamlined and effective spine care, ultimately enhancing patient outcomes.

Key Words: Spine care; Spine artificial intelligence; Machine learning diagnostics; Precision rehabilitation; Robotics surgery

Core Tip: Artificial intelligence and machine learning are transforming spine care by improving diagnostics, surgical precision, and personalized rehabilitation. These technologies enable early risk assessment, accurate implant placement, and adaptive therapies, though challenges like data limitations and ethical concerns remain. Future advancements in multi-modal artificial intelligence systems promise enhanced precision, accessibility, and cost efficiency, revolutionizing patient outcomes in spine care.

INTRODUCTION

Back and neck disorders are common issues that many people face, and they can significantly affect everyday activities. Low back pain, scoliosis, and spinal stenosis are significant health problems and place a heavy burden on patients and healthcare systems worldwide, including spinal disorders[1]. Amongst the most common causes of disability worldwide is low back pain, with 60%-70% of the population in some instances experiencing low back pain in their lives. These are complex, multifactorial conditions, and the designated pathophysiologies and genetic vulnerabilities are primarily biomechanical. In the case of untreated scoliosis, the most frequent diagnosis during adolescence is thoracolumbar deformities of the body, and important pulmonary and cardiovascular compromise are possible[2]. The number of older people with immobility restrictions and disintegrated life standards was also subjected to spinal stenosis, and surgical treatments were required. Most studies transparently discussed their methodology, although some had constraints with limited sample sizes and no longitudinal data. The risk of bias was moderate for the accuracy of diagnosis in all studies, as several used retrospective data. Moreover, studies assessing artificial intelligence (AI) in surgical planning often did not have real-world validation testing. As described above, spine care is an area where more prospective studies are required to validate the use of AI/machine learning (ML) technologies. These diseases signify a pressing need for appropriate diagnosis techniques, specific treatments, and patient rehabilitation[3]. These spine disorders are a significant socioeconomic burden because of the decreased workforce productivity as well as the very high cost of treatment. Similarly, low back pain is a serious public health issue with many economic impacts[4].

However, medical technology advancements have not eradicated the problems of diagnosing and treating spine disorders. Conventional imaging techniques, including magnetic resonance imaging (MRI), computed tomography (CT) scans, and radiography, are required to provide information about structural deformity of the spine[5]. However, most of these diagnostic instruments do not reproduce the anatomical dynamics of the “moving parts” and cannot simulate the properties of the spinal diseases in a clinical situation. However, this aspect is a limitation that challenges clinicians in developing patient-specific treatment plans. There is no standard for comparing these diagnostic tools due to the variety of diagnostics and treatment recommendations[6].

While embedding AI in spine care, an important concern is looking for a balance between patient privacy, potential stereotyping within AI systems, and the need for self-explanatory AI frameworks. The manuscript presents these issues and calls for cooperation between different fields to ensure that AI technologies are not mishandled. Furthermore, we examine the evolution of AI in spine care, which includes predictive modeling, tailored rehabilitation therapy, and automated off-site patient supervision services, particularly focusing on patients from marginalized groups. Nothing is different in terms of the complexity of spinal disorder treatment. In physical therapy and pharmacologic treatment, differentiating the response to the patient is the basis for inconsistent non-surgical treatment outcomes. It can work for some patients if planned carefully and with the eye of a magnifying glass on its distinct and highly significant risks for best results[7]. Until now, no reliable prediction tools exist to determine treatment efficacy, disease progression, or prognosis, whose limited progress in the long-term management of spine disorders thus comes into being. However, no protocol of treatment results in a high relapse rate and poor clinical outcomes for patients[8].

This is a complicated activity for spine treatment to balance evidence-based medical practice with patients’ unique needs. First, given that spine pathologies generate abundant clinical data when looked at diligently regarding diagnosis and treatment. However, since the existing methods do not use composite medical data due to their unavailability, they cannot efficiently process this data and reap the patient care optimization opportunities[9].

In this paper, we elaborate on the need and research that would lead to understanding the potential of using AI and ML technology for spine care solutions to address the current problems[10]. AI and ML technologies are still young and help them with easier paths for recognizing complex dataset patterns and applying solutions beyond human capacity. In spinal healthcare, experts, increasing interest has been raised in new applications that promise improved diagnostic precision and sophisticated treatment plans, leading to better outcomes and more outstanding health rehabilitation[11].

This research aimed to understand how AI and ML technologies can make it easier to evaluate such spinal conditions unbiasedly and comprehensively, and circumvent issues with the current diagnostic methods. This paper aims to address whereby predictive analytics could be utilized for the identification of high-risk populations and to monitor the progression of a disease over time. This manuscript emphasizes the potential personalization of treatment interventions through the long-term outcomes of the patients by utilizing multimodal AI systems that integrate imaging, clinical, and genetic data for a more holistic spine care. AI models predict disease progression anticipatively through symptomless stages in high-risk populations. We try to fill in the gaps in previously lacking literature, which underpinned our further investigations into preventative spine care in conjunction with predictive analytics-powered AI. Nowadays, AI algorithms can assist in generating tailored treatment plans and improve surgical outcomes[12]. The paper has stated that post-treatment rehabilitation AI and ML technologies generate adaptive care plans to meet the specific needs. It explained the need for AI and ML technology to converge and redesign precision and sustainability to improve care for patients with spine disorders.

METHODOLOGY AND SCOPE

The strategy of this systematic review has been prepared with precision and meticulousness. A thorough search of literature was conducted through the following databases: PubMed, Scopus, IEEE, and ScienceDirect, and the phrases “AI in Spine Care”, “ML in Spine Care”, and “Emerging Technologies in Spine Care” were used. Research was conducted on whether they utilized AI/ML technologies in spine care and fulfilled these requirements: Clinical applicability, peer-reviewed research, and the complete published text. Included in data extraction were study design, application of AI/ML in the study, sample size, patient demographics, and important findings. The methodology guarantees that the AI technology review is systematic, and other scholars can recreate the process. All peer-reviewed articles and clinical trials that utilized AI and ML innovations in the spine were included in this study. The following data sources were used for both clinical and computational domains to search the peer-reviewed articles: PubMed, Scopus, IEEE, and ScienceDirect, with keywords “Spine Care technologies”, “AI in Spine Care”, “ML in Spine Care”, and “Emerging technologies in Spine Care”. The study synthesized knowledge from different fields, including spine biomechanics, medical imaging, clinical spine practices, and computational techniques like AI and ML.

SPINE ANATOMY AND BIOMECHANICS

Vertebrae, intervertebral discs, and ligamentous and muscular tissues work together in the human spine to support structures that enable movement and safeguard the spinal cord. The biomechanical properties of the structure create a remarkable balance of stability and flexibility, enabling daily activities and functional load-bearing capability. Spinal care diagnosis and treatment methods depend heavily on the complex relationships among spinal components, which have become key objectives for AI and ML studies. The combination of computational analysis of spine structure and function through AI and ML technologies leads to more precise diagnostic imaging and provides superior insights that traditional methods cannot deliver[13].

PERSONALIZED SPINE CARE THROUGH MULTIMODAL DATA INTEGRATION

The AI and ML models incorporated into spine care have greatly improved the field’s standards, especially by combining different data sources. Multimodal data is a blend of imaging and genetic data, giving clinicians a complete picture of the patient’s condition. Integrating these heterogeneous data forms can modernize diabetes management by expanding the individualization of therapy and selection of diagnostic procedures, transcending the limitations of standard approaches to diagnosis and treatment[14]. The use of AI and ML to examine complex datasets that were previously challenging to merge and make sense of informs the progression in spine care[12].

Clinical data integration

Clinical information, which incorporates comprehensive patient data, medical examination records, and the markers of the disease’s progression, has always been significantly useful in dealing with spine conditions. Nevertheless, most of these information modalities are separate, which impedes their multifactorial employed decision-making, treatment-scheme-creating capabilities. Multimodal AI systems integrate disparity, merging clinical data with imaging and genetic data to better understand the patient’s condition, thus overcoming these limitations. For example, a patient’s clinical history includes past surgical interventions, other relevant comorbidities, and response to treatments, which can now be processed alongside more objective data from sophisticated imaging methods. With such an amalgamation of data, it is possible to anticipate disease progression and enhance predictive analytics. Treatment calibration can be more precise, improving outcomes[15].

Imaging data integration

Various imaging techniques, including MRI, CT scans, and X-rays, have been used to evaluate spinal deformities and diagnose degenerative disc disease, scoliosis, and spinal stenosis. Nevertheless, these imaging methods emphasize pathological structural features and often overlook functional biomechanical movements and load distribution within the spine. Multimodal AI systems enhance imaging data analysis through image processing, deep learning, and other sophisticated techniques. These advanced systems can segment and classify spinal abnormalities, detect early degenerative changes, and even predict future progressions. In addition, integrating clinical data with imaging enables AI to provide personalized assessments of imaging findings by pinpointing specific spinal regions vulnerable to degeneration or injury, thus allowing precision intervention[16].

Integrating genetic data with predictive analytics

Incorporating genetic data into spine care adds another layer of complexity, but its integration into multimodal AI systems can further enhance treatment personalization. Specific genes have now been linked to certain spinal pathologies, such as osteoporosis and disc degeneration, owing to advances in genomics. Multimodal AI can now merge such genetic determinants with clinical and imaging data in order to forecast the patient’s risk of developing certain conditions. For instance, genetic susceptibility to degenerative disc disease or scoliosis can be determined much earlier in life, long before any clinical features begin to show. Enhanced predictive models that incorporate genetic information assist in refining the most optimal intervention strategies, which range from preemptive actions to bespoke therapeutic measures[14].

Moreover, multimodal data fusion can provide interventions even before clinical symptom emergence. For example, AI-based analytics may flag a patient with a known SNP and a history of back pain as likely to develop chronic spinal conditions, enabling timely mitigation. By synthesizing these data with AI systems, advanced spine health management becomes possible to stop the disease from progressing and reduce the need for invasive interventions.

The future of multimodal AI systems in spine care

Multimodal AI systems can diagnose and treat spine care ailments structurally. These systems can reveal new insights by evaluating intricate and extensive datasets that were impossible to obtain using conventional means. The integration of spinal imaging, clinical history, and genetics enhances the precision of diagnosis and enables the formulation of tailored therapeutic regimens[17]. Adopting these systems enables predictive and preventative interventions that can identify spine-related conditions at earlier stages and reduce the need for invasive procedures. AI systems can analyze enormous clinical datasets to create individualized care pathways, improving patient outcomes. An opportunity exists to get ahead of the curve by utilizing multimodal AIs by transforming proactive spinal care into a strategic, patient-first field.

SPINE DISORDERS

Spinal disorders encompass various processes with disparate pathophysiological and clinical events. One such disorder is the herniated discs, made of disc material that migrates into the surrounding neural element, producing pain and neurologic symptoms. The most common degenerative spine disorders, mainly when they involve chronic back pain, are osteoarthritis (best known by its nickname, arthritis) and degenerative disc disease, which cluster in older people and limit their function[18]. Most commonly, scoliosis appears during adolescence as a side-to-side curve of the spine that puts the spine out of alignment and can lead to severe deformity if left to progress. Most fractures and spinal cord injuries caused as a result of accidents, falls, etc., leading to spinal trauma, need emergency treatment[15]. There is a considerable medical dilemma in primary or metastatic spinal tumors since they interfere with the spinal neural function and structural integrity.

The case studies examined in this review demonstrated that the predictive models AI applied in the preoperative phase of the procedures enhanced the results achieved[19]. The outcomes of the studies that were evaluated have been integrated in Table 1[14,16,20-32], which shows the comparison of traditional methods and AI/ML-enhanced methods of spine care for different spinal conditions. Most important results from the assessment of diagnostic precision, surgical results, and rehabilitation exercises performed afterward are included as well. AI techniques enhanced the accuracy of the diagnosis, and the role of subjectivity in the interpretation of results was softened dramatically (Figure 1). Deep learning algorithms at least accurately identified and classified vertebral fractures and disc herniations as radiologists did, therefore, their implementation did improve accuracy.

Figure 1 Comparison between traditional and artificial intelligence-based medical imaging processing. Artificial intelligence methods enhanced image and data processing. AI: Artificial intelligence.

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.

All spine disorders are diagnosed and treated through a multidisciplinary approach. In this regard, diagnostic imaging methods should be applied alongside respective surgical treatment and post-operative rehabilitation methods[33]. When evaluating the spine, no test is based on any imaging method – when MRI presents irregularity with exact anatomical resolution, CT shows the complete optical perception. The diagnostic tools are helpful but rely on individual interpretations[17].

When treatment is extreme and conservative therapies have not worked, doctors often use surgical approaches, decompression procedures, and fusion and disc replacement techniques. When planning surgical procedures, it is crucial to consider the anatomical details, functional capacities, and unique characteristics of each patient[34]. Because these treatments result in an AI development built on precisely executed observations, this yields the most precise spaces of any training. Consequently, remediation strategies involve pain management techniques and physical rehabilitation methods to prevent recurrence and restore functionality[21]. These produce favorable outcomes in standard development pathways but differ in patient adherence depending on disease outcomes. Therefore, they should best be followed with individualized medical strategies to improve outcomes.

However, the barriers to the accurate delivery of such treatments for spine disorders have not yet been overcome through the adoption of medical technology advances. Interpatient variance leads to differences in clinical outcomes, subjective imaging interpretation, and complex treatment planning. AI and ML rely on the data to create practical insights and automate diagnostics, further facilitating the production of customized treatment baselines that respond optimally to contemporary challenges[35].

THE ROLE OF AI AND ML IN SPINE CARE

AI technologies and ML systems are advancing rapidly, leading to new possibilities in the healthcare industry, with spine care being a significant application area. Spine disorders are complex conditions with multifactorial backgrounds representing significant obstacles to their diagnosis and treatment. However, traditional methodologies fail to provide much success since they rely on human judgment, which, in turn, results in subjective interpretation and overly rigid, static patterns of operations. AI and ML remove the existing limitations by offering solutions based on data analysis, which work effectively and evolve to strengthen human expertise. Medical technologies enable practitioners to shift from standardized treatment to precision medicine, in which therapy is adjusted according to the patient’s characteristics[36].

It allows clinicians to analyze extensive datasets containing lots of data and be able to find the hidden patterns and surface those, to be able to predict the disease outcome, and to be able to prescribe exact, personalized treatment protocols. However, due to differing disease presentation and treatment responses, precision medicine is critical for spine care, which is more often more complex in care pathways[37]. The application of AI and ML technologies to imaging diagnostics, surgical planning, and rehabilitation strategies aims to address the needs of patients with spine disorders.

Diagnosis

AI in medical imaging: AI in medical imaging of spine care is the ability to automatically segment and classify spinal abnormalities. MRI and CT, traditional imaging techniques, have given the ability of higher resolution images, but as clinicians have to interpret these images relying on their expertise, the diagnosis may vary, and errors may happen[38]. These AI systems derive from supervised ML, where the AI learns to detect and outline important anatomical structures such as vertebrae, intervertebral discs, and neural components, among others[39].

Spinal component segmentation is automated using AI technologies, which help medical professionals detect fractures and disc herniations, identify spinal stenosis areas, and speed up diagnostics. Detection of vertebral fractures or degenerative changes is now done with an AI model detection accuracy that is at least comparable to that of human radiologists[40]. This capability enhanced diagnostic consistency, improved efficiency, and minimized variations between clinical interpretations.

Deep learning applications for enhanced diagnostic accuracy: The field of spine diagnostics has been impacted by the introduction of deep learning, which uses convolutional neural networks to analyze medical imaging data. Convolutional neural networks show excellent performance in analyzing spinal images that vary greatly in their anatomy, owing to their ability to perform pattern recognition in complex data sets[41]. These models are used extensively to detect disc degeneration, scoliosis, and spinal tumors using automated detection systems.

The models have also shown the ability to process thousands of MRI and CT scans to detect subtle disease indicators that human evaluators cannot spot. By allowing the processing of large datasets quickly, these models give an accurate diagnostic result. Recent advances in the modern application of medical imaging and deep learning in multi-modality allow the union of clinical and genomic data with medical imaging to improve detection accuracy[20]. The approach excels in complex medical situations such as multi-stage degenerative spine disease, where database insights are needed to support clinical decisions.

Predictive analytics for early detection: ML and AI aid physicians in taking proactive measures in spine care by identifying patients with the potential to develop spinal degenerative diseases or injuries. The predictive models take longitudinal patient data, wearable device readings, and imaging studies to predict disease advancement and complication probability[42]. Thus, ML algorithms help spot early notifications of such medical conditions, including degenerative disc disease and scoliosis, before the clinical symptoms appear, paving the way for early treatment interventions[43].

These predictive instruments are responsible for much of the injury prevention. The spinal injury risk is evaluated with AI systems that look at biomechanical patterns, containing information on load distribution, posture, and gait patterns amongst athletes and people who do strenuous physical work[44]. Results provide custom health interventions, like ergonomic workspace upgrades or exercise routines, to minimize the number of spinal issues in high-risk populations[37]. The examples of various applications of AI/ML in spine care are summarized in Table 2[15,22,24,45-57].

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.

SURGICAL PLANNING AND NAVIGATION

ML-based preoperative planning

However, the complexity of surgical decision-making now involves ML to assist in the preoperative planning, and now the most essential tool in the spine surgeries’ preoperative planning phase. In the application, ML algorithms leverage the massive database regarding patient demographics and clinical histories, imaging studies, and past surgical outcomes to bring insights that can improve current surgical strategies[58].

Outcome prediction is one of the most important application areas for ML technologies during the preoperative planning phase. Based on historical data analysis, ML models allow for predictions regarding the success rate in achieving surgical results, such as pain relief and restoration of spinal stability. These predictive insights can help surgeons accurately predict what to expect regarding wound healing for a given fixed period with their patients and also identify those who will benefit the most from specific surgical methods. The ML models, through risk assessment algorithms, identify potential risks such as adjacent segment disease and hardware failure so that surgeons can preemptively change how they operate to minimize these risks[59].

It brings enormous benefits to the first use case of implant optimization by providing personalized implant size, type, and positioning suggestions. For successful implantation of spinal implants such as pedicle screws and interbody fusion devices, it is still crucial to have the devices correctly positioned to protect the biomechanical integrity of the spine[60]. ML algorithms use biomechanical and anatomical data to calculate the best screw trajectory and depth, minimizing errors like neural damage and incorrect hardware placement. The simulations from these models allow for simulations of different surgical approaches related to biomechanical effects that improve surgical planning and after-surgery stability[61].

The ML technology already assists surgeons in deciding the correct surgical technique. The analysis of disease severity and patient health profile, combined with forecasted recovery periods by predictive algorithms, decides how minimally invasive procedures are compared to open surgeries. With the detailed approach, healthcare professionals can create surgical plans that better match their patients’ traits, resulting in improved results and decreased surgical risks[62].

The practical use of ML algorithms is demonstrated in forecasting surgical results after lumbar spine operations. These models predict the chances of successful pain reduction and functional recovery of an individual patient, considering the patient’s age, the presence of other illnesses, and the severity of spinal pathology[63]. As an alternative, surgeons can use dynamic stabilization if ML models identify patients for whom adjacent segment disease risk is higher following spinal fusion surgery.

The analysis of pedicle screw positioning by ML is used to help surgeons to determine the best placement for implants. The algorithms are 3D convolutional neural networks that interpret preoperative imaging data to suggest precise screw insertion paths[64]. Spinal surgery database training resulted in the success of ML systems in reducing the errors in placement of pedicle screws, improving both surgical safety and patient outcomes[65]. These systems open the door for surgeons to test and determine biomechanical stress patterns of the spine and the best placement strategy for optimal implant stability after surgical procedures.

Medical researchers have employed ML to help surgeons choose the best surgical approach. It shows that predictive models assist medical practitioners in determining whether to select minimally invasive discectomy or open surgery for degenerative disc disease patients[66]. By integrating patient-specific data, models can guide a surgeon to such surgical interventions supported by clinical evidence while satisfying the needs of each patient.

AI-enhanced intraoperative guidance

AI has transformed the surgical landscape in spinal procedures, allowing surgeons to operate with better real-time accuracy and precision. When a surgeon has limited access to the surgical area and has limited reach to the surgical area, it becomes necessary to perform the surgery with advanced technology. AI systems combine preoperative imaging data with intraoperative data to generate dynamic 3D models of the surgical field. Medical reconstructions allow surgeons to navigate complex spinal anatomy with great precision[67].

AI in medical applications is utilized by robot-assisted surgery as one application area. AI systems develop robotic systems that are more precise and stable than surgical processes that are manually conducted. Surgical systems integrated with these utilize preoperative planning data to execute the precise movement operation during a surgical operation. Pedicle screw alignment and insertion by robotic arms can achieve submillimeter accuracy and maximizes proper screw placement, and minimizes nerve and vascular damage[68]. The application of AI on robotic systems allows for better ongoing surgical adjustments based on instant imaging feedback, with the autonomy to change the surgical plan during intraoperative discovery.

In surgical operations, surgeons get real-time linked AI navigation systems slotted together with robotic technologies. The intraoperative imaging methods, including fluoroscopy, intraoperative CT scans, 3D ultrasound, and others, are coupled with intelligent algorithms to give current surgical direction. AI systems that notify surgeons of unexpected pathway divergences work with the fixation positioning of real-time surgical tools with patient anatomy. This feedback mechanism helps reduce errors and improve patient outcomes by maintaining compliance with surgical plans[69].

In this respect, intraoperative guidance systems enhanced by AI make the difference since they considerably reduce patient and surgical team radiation exposure. Intraoperative imaging is commonly achieved using widely available X-ray techniques that generate multiple fluoroscopic images throughout the procedure and thus expose the patients and operating staff to cumulative radiation. Better imaging protocols on the part of AI systems mean less and less powerful images can be taken and remain accurate diagnosis[70]. A complete three-dimensional anatomical representation is created by AI platforms from small imaging collections, such that additional scans are not required.

The significant applications of robotics-assisted surgical systems include the Mazor X and ROSA Spine platforms. Surgical tools here are supported with extraordinary precision by AI. The three-dimensional representation of the patient’s spinal structure was built via the combination of the preoperative CT scan with the intraoperative fluoroscopic imaging via the Mazor XTM system[71]. Medical staff can view a map for accurate planning and executing of pedicle screw trajectories; this reduces liability for surgical complications. The research shows that robotic-assisted AI guidance can achieve screw placement accuracy rate greater than 95% in excesses of traditional freehand technique[72].

There is a solution to navigation used by Medtronic known as the Stealth Station, which delivers live data back to surgeons during operative procedures. These systems can continuously update virtual 3D anatomical models dynamically during the operation, using continuous intraoperative imaging, and maintain millimeter-level tracking precision of the surgical instruments[73]. AI guides the deformation of the spine osteotomy and alignment during spinal deformity correction and reduces the chance of patients having remnant deformities after surgery using navigation systems.

Radiation reduction is possible during surgical procedures, as intraoperative imaging methods show. Using much fewer fluoroscopic scans, similar AI algorithms created by researchers enable precise navigation by performing image optimization. C-arm systems are equipped with modern AI technology to develop 3D anatomical views from limited imaging inputs, resulting in fewer scans while providing accurate results[74]. However, the use of AI technologies during complex spinal tumor resections provides critical value because such use minimizes the need for re-imaging that may unnecessarily increase radiation exposure to the patient and operating team.

Broader implications for spine surgery

The implementation of ML and AI for surgical planning is much more than technological innovation that enables a new method of doing spine surgery, it is the creation of a fundamentally new practice. The interventions employ these computational approaches to solve the persistence of surgical challenges related to high outcome volatility, the high number of complications, and inefficiencies in manual methods arising from human error[75]. AI and ML systems make spinal surgery more precise by offering safer interventions that result in better outcomes and are tailored to a patient’s needs. The analysis of preoperative imagery of scoliosis patients with AI helps medical teams determine the level of surgical intervention that the patients would require. It offers improved postoperative alignment, is guaranteed to minimize errors in the number of corrections that lead to wrong correction amounts, and enhances surgical planning[76].

Minimally invasive transforaminal lumbar interbody fusion is made more streamlined by improving the planning process of such operations, using ML in conjunction with AI technology. AI applications utilize preoperative imaging data and biomechanical information to improve the accuracy of the placement of surgical cages. This may result in a reduced incidence of endplate fractures or cage subsidence[77]. Augmented reality and virtual reality (VR) will improve visualization that exists in operations and provide improved training for surgeons when AI-powered systems are integrated. Due to these advances, AI is expected to play a more significant role in spine surgery and other areas of medical practice[78].

REHABILITATION AND POST-TREATMENT MONITORING

To effectively recover patients on spine care, rehabilitation practices, and post-treatment monitoring are crucial in preventing complications and recurrences. The latest technological advancements in AI and ML have enabled the enhancement of processes by enhancing the rates of perfection with the ease of personalization and efficiency[79]. Medical rehabilitation and patient monitoring systems can be formed in a new light, using real-time data and predictive analytics to implement flexible treatment plans for patient needs.

AI-driven rehabilitation programs

AI systems have enabled rehabilitation programs to broaden their treatment choices to include customized contemporary strategies matched to the one-of-a-kind health demands of each patient. Patient anatomical structure differences and intensity of their injury or varied recovery speed are generally not considered in standardized spine disorder rehabilitation protocols. AI breaks through these limits by understanding healthcare data with image scans and biomechanical assessments, developing the best exercise routines and therapeutic approaches for each individual[37].

During this kind of rehabilitation practice, wearable sensors measure a patient’s movement patterns, his/her posture, and activity intensity. Rehabilitation data is collected and processed by real-time AI algorithms to provide feedback, such as accuracy and quality of the exercise, allowing the users to perform the movements correctly so that they do not have to sustain more injuries. Data insights enable therapists to adjust protocol dynamics to enhance treatment outcomes and accelerate patient recovery[80]. According to numerous research studies, AI applications in postoperative spine rehabilitation encourage patient adherence, decrease the associated costs, and improve patients’ functional recovery results.

Remote monitoring and virtual rehabilitation

The implementation of AI in remote monitoring technologies has broadened the horizon of high-quality rehabilitation services to remote area patients and underserved patient populations. Mobile applications and virtual rehabilitation platforms’ VR interfaces can deliver exercise instruction using AI algorithms. These systems have real-time correction features that track patient performance and replicate the direct therapy session[81]. AI-powered home exercise systems for lumbar discectomy patients reduce the need for clinic visits.

With the help of wearable technology and AI, continuous surveillance throughout rehabilitation processes is allowed. These databases integrate innovative braces, motion sensors, and fitness trackers to store patient data on the range of motion, gait patterns, and activity. The data analysis helps machines identify recovery patterns and detect irregularities compared to standard healing paths, which assist clinicians in identifying potential medical issues within the patient, such as slow wound healing or incorrect movement habits[82]. The proactive strategy permits the deployment of control interventions on problems that have been detected.

Predictive analytics for long-term monitoring

Clinicians should monitor patients with chronic spinal issues or past surgeries for early recurrence or complications due to higher risk factors. AI and ML generate predictive analysis of the present and archived data sets, resulting in extended monitoring duration. Predictive models have shown that they can successfully identify patient groups that are at high risk for recurrence of disc herniations after initial surgery, as well as post-spinal fusion surgery, and adjacent segment disease. Predictive insights are used to set up preventative measures with targeted physiotherapy or lifestyle modifications to reduce patient risk factors[83].

In pain management, clinical researchers use AI-driven tools to assess pain information, such as patient-proffered data, including imaging study results and drug information, to determine possible treatment outcomes. Using AI algorithms, the relationship that links poor pain control or treatment side effects can be detected and refined so that treatment remains safe and effective[84].

Enhancing patient engagement and outcomes

These AI -powered systems lead to better patient care and provide patients with more participation and satisfaction. Due to this ability to provide real-time, individualized, and tracked progress, these technology systems allow patients to participate individually in their recovery journey. The use of reward mechanisms in virtual rehabilitation platforms for gamification has been proven to increase patient group adherence and motivate younger patients toward exercising routines[85].

AI systems assist medical professionals in interacting more effectively with their patients. By using automated reporting systems, patients can instantly give their progress updates to clinicians and make more consistent and valuable exchanges between them. The feedback mechanisms continue to build patient trust and ensure that rehabilitation strategies are aimed at individual patient targets as long as care quality is enhanced[86].

AI-powered wearable devices for real-time posture correction and rehabilitation monitoring

AI-powered wearables support posture, movement patterns, and patient recovery in real time. They use advanced sensors and AI algorithms for continuous data collection and analysis, providing precise, personalized care to healthcare professionals. The patient’s posture is tracked, and spinal alignment errors are detected using wearable sensors and smart braces. The real-time data is analyzed by the AI algorithms and sent to the patients as visual or auditory signals. The advantage of this capability is that it assists in maintaining good posture during regular activities of daily living and will lessen back stress to prevent further spine issues[87]. Rehabilitation devices assist patients in performing specific exercises at the appropriate rate, which prevents the risk of injury and improper movement to reduce recovery time.

Remote patient monitoring systems using ml for post-surgical recovery and adherence tracking

ML-powered remote patient monitoring systems change post-surgical recovery because they provide ongoing supervision and intervention, which are variable to each person’s changing needs. These systems are supplied with recovery metrics, such as activity levels and patient compliance with rehabilitation protocols and pain scores, wearable devices, mobile health apps, and electronic health records[88].

ML methods employed by remote monitoring systems predict when patients deviate from their anticipated path of recovery. The ML algorithms serve as pattern recognition tools for analyzing patient activity data to detect early signs of delayed healing, such as decreased patient mobility and increased pain when performing some exercises. Alerts issued by systems about patient risks to healthcare providers offer opportunities for early treatment of patients before complications arise and enhance patient outcomes. Adherence tracking works well with ML because it can detect rehabilitation barriers and propose solutions to overcome them[89]. Patient activity analysis done through the ML model identifies deviations from prescribed exercises based on whether the movement intensity indicates the patient is incorrectly performing inconsistent movements. This system helps provide customized reminders, motivational messages, and modifications to rehabilitation plans to increase adherence rates[90].

Clinicians and patients use remote monitoring systems to improve communication channels. These systems, powered by ML, offer automated reporting features that summarize recovery data and highlight problematic areas, aiding clinicians in making informed follow-up consultation decisions[91]. The patient data stream continuously flows in and creates a proactive healthcare environment that keeps recovery strategies in line with current patient requirements.

Advanced imaging techniques and AI algorithms

The reason is that AI algorithms can bring tremendous advancements in the analysis of medical images by their nature. convolutional neural networks have advanced deep learning models and have made it possible to automate medical systems to detect spinal problems like herniated discs, vertebral fractures, and spinal tumors with the highest accuracy. Instead, AI algorithms can look through vast MRI and CT scan datasets to quickly deliver complete analysis outputs to radiologists[92].

New medical imaging technology has enabled systems that merge MRI and CT scans with X-ray data. The AI technology combines multiple datasets to create a precise three-dimensional spinal model, a complex evaluation of spinal conditions like scoliosis and multiple-level degenerative spinal diseases[16]. Such latest imaging solutions reduce diagnostic variations, increase the detection of subtle abnormalities, and expedite medical intervention.

Predictive and preventive spine care

The development of advanced spine care sees its application of predictive analytics techniques primarily. In this case, advanced AI protocols recognize those prone to scoliosis and degenerative disc disease when these diseases do not cause symptoms. Combined patient genetics, motion mechanics data, and behavioral patterns support the analysis models to generate practical medical suggestions and help preventive medical strategies[93].

ML algorithms can detect those at risk for chronic back pain through workplace ergonomic patterns combined with physical activity measures and medical records data. By predictive analysis, we generate customized prevention techniques, including physiotherapy programs and furniture arrangements in the office, to reduce the total number of spine disorder occurrences[94].

PREVENTIVE AI AND WEARABLE DEVICES IN SPINE CARE

AI and wearable technologies are most promising in preventive healthcare, especially in managing spine disorders. While the focus of research has been on the role of AI in diagnosis and treatment planning, predicting and preventing the advancement of spinal disorders is an underexplored area that holds tremendous promise. Innovative robotic assistive braces, smart sensors, and motion-tracking devices fall under AI-powered wearables, enhancing proactive spinal healthcare maintenance[95].

Real-time monitoring and early detection

AI-enabled wearables can monitor spinal health metrics to detect abnormalities long before clinical symptoms appear. These devices monitor a patient’s posture, movement, and gait, which AI systems process. Many data points can be analyzed so that the AI algorithms can predict the probability of degenerative disc disease, scoliosis, or spinal stenosis. For instance, wearables can detect abnormal posture or uneven gaits, which may indicate early development of scoliosis or lumbar disc degeneration. Recognition of these signs allows clinicians to preemptively apply tailored exercises, ergonomic interventions, or lifestyle alterations before the condition worsens[96]. One of the most distinguishing features of wearable AI technologies is their perfect capability to gather continuous, longitudinal data useful in tracking a patient’s condition over time. Unlike obsolete diagnostic methods based on periodical assessments when a patient’s health status is stagnant, this information is critical for accurate predictive models and delivering adaptive care tailored to the person’s needs. Wearables offer a more dynamic approach and real-time assessments of spine health rather than periodical ones[97].

Personalized preventive interventions

The advancement of AI technology in wearables offers the most promise in providing proactive and personalized preventive care. AI can formulate custom strategies for patients by analyzing data collected through sensors from wearables to help mitigate risks linked with spinal complications. For example, when AI identifies early signs of postural and gait abnormalities, it can recommend tailored exercise plans or specific physical therapy sessions. Moreover, AI systems linked to wearables can notify patients and caregivers when proactive corrective actions have to be taken, helping to avert significant deterioration. These preventative actions may entail providing feedback on excessive or insufficient spinal support, static prolonged positioning, and even proactive spinal stabilizer muscle training. AI integration into wearables allows these interventions to be bespoke rather than general, tailoring the guidance to the individual’s health metrics and medical needs[98].

The role of predictive analytics in preventive spine care

Besides offering real-time reporting, AI and ML technologies can project the progression of spinal conditions by analyzing large datasets. AI systems can create predictive models based on clinical data, imaging results, and genetic information to estimate disease progression. Chronic back pain and degenerative disc disease are some diseases that predictive analytics can help pinpoint patients who are predisposed due to genetics, lifestyle, and previous medical history. This permits clinicians to act much earlier and enables preventative treatment or monitoring to decelerate disease progression[99]. Moreover, AI can aid in identifying high-risk populations such as laborers who perform physically demanding tasks or those with a genetic predisposition to spine-related conditions. AI systems can then devise tailored preventive strategies, such as prescribing individualized physical therapy regimens, ergonomic modifications at work, or lifestyle changes that minimize physical stress on the spine for these patients[100].

Impact on healthcare efficiency and accessibility

Integrating preventive care strategies with spinal care through AI-powered analytics and wearable devices can potentially improve patient outcomes alongside healthcare system efficiency simultaneously. Identifying and dealing with an issue early often minimizes the need for later expensive and resource-heavy treatments. When used for spinal care, if applied promptly, preventative methods can mitigate the progression of spinal ailments to a stage where surgery or prolonged medication would be needed. Reducing the required high-cost remedial steps will greatly enhance affordability and accessibility to healthcare services, particularly in underserved regions of the country where traditional diagnostic methods are not available[101]. Furthermore, patients can utilize AI-powered wearable devices in virtually any location, making these devices convenient and flexible. The electrocardiogram monitor allows a greater range of patients to actively participate in managing their overall health. Individuals are better equipped to make decisions about their spine health due to active AI recommendations and data analytics conducted in real time.

Proactive uses of AI technology in spine care

While continuing advancements in technology, such as AI and wearables, capture the attention of many, there is even greater opportunity lying within the field of proactive spine care. AI technology holds promise in long-term outcomes and improvements in spinal health for patients, as well as in alleviating the overall strain that spine health issues pose on the healthcare system. Moreover, AI shows potential in predicting, preventing, and monitoring the development of spinal disorders. Predictably, the usefulness of AI technology geared toward healthcare will improve spine care practices. By providing real-time data, AI wearables and other monitoring technologies can prevent spinal disorders from developing or worsening, improving healthcare efficiency[97,100].

BENEFITS AND IMPACT ON CLINICAL OUTCOMES

Advanced AI coupled with ML technologies has now become available to lead the spine care practice to a new era in its development. Such a great innovation is represented by robotic surgery systems and AI based on augmented reality in spinal procedures, such as VR powered with AI in physiotherapy. AI with ML inside spine care applications is generating transformative benefits, huge clinical improvements, and better overall standards of care[102]. From diagnosis, treatment design, and rehabilitation, which currently suffer from reachability, backward-looking diagnostic results in improved care precision and efficiency of spine treatments, focusing on the patients[103].

AI-based virtual physiotherapy

Virtual physiotherapy platforms are developed on an AI system capability that allows rehab practices to improve through virtual physiotherapy by the ability to adjust physiotherapy to a patient-specific condition. Time-sensitive data from wearable sensors activates motion-tracking cameras that feed information to AI algorithms that provide instant feedback (along with corrective advice) during supervised patient exercise sessions. Specialist-designed systems are used for patients to complete their therapy while reducing physical checkups simultaneously to increase the service availability and efficiency of rehabilitation[104].

Enhanced diagnostic precision and reduced human error

The applications of AI and ML in spine care have led to considerable improvements in the precision of medical diagnoses. Standard diagnosis through image typically relies on personal interpretation, is identified as the source of diagnosis variability, and increases the error rate. Deep learning-based AI algorithms offer an automated examination process that is consistent in terms of performance, causing the analysis results to be precise (Figure 2). AI models trained on large training datasets (MRI and CT scans) and manual reviews can reveal subtle abnormalities, such as early degenerative changes or minor vertebral fractures[105]. These technological systems do not make human mistakes and increase diagnosis speed; therefore, healthcare professionals can take faster and better intervention steps. This strengthens the clinical benefits for the patients since successful treatment requires timely and precise identification[106].

Figure 2 Multimodal model. Collect the various data types, integrate them into clusters, and perform the machine learning data analysis and prediction.

With deep learning AI algorithms, measurement challenges of examining the texture are overcome by automating the examination processes and thus delivering consistent and accurate analysis. These models can be trained on large datasets like MRI and CT scans, which are trained to detect subtle abnormalities like early degenerative changes and minor vertebral fractures, which are often missed during manual reviews (Figure 2)[51].

Enhanced patient-specific treatment planning

With the power of AI and ML, treatment planning has been modernized such that treatment planning is done using patient-specific data culled and custom treatment solutions evolved and formulated. The AI algorithms differ from traditional standardized treatments because they aggregate data sets, including imaging results, clinical history, genetic information, and lifestyle factors, then train the algorithms to develop personalized treatment strategies[107].

The models allow for predicting how individual patients will respond to surgical or non-surgical treatments, thereby helping clinicians choose the best treatments. Precise implant placement optimization through advanced AI technology towards its application in surgical planning plans, predicting postoperative results to lower the complication rate and reduce healing time[77]. Personalized medical treatments will increase treatment results and patient satisfaction because the care is offered to him according to his needs and preferences.

AI in telemedicine and remote health care solutions for spine care

AI in telemedicine applications can reach remote and disadvantaged communities in spine care[108]. In contrast, AI in remote healthcare monitoring systems delivers broader accessibility to quality care, especially for those in distant or economically disadvantaged communities. Because of virtual technology augmented by AI, patient access to consultations, examinations, and rehabilitative care at any place is still possible, but immediately[109].

AI capabilities are found in modern telerehabilitation solutions that let researchers create personalized workout routines for their subjects and track user performances using a sensor attached to said user to provide instant feedback[104]. These systems help patients remain healthy in their homes, where they receive expert medical attention[108]. Predictive analytics are incorporated with remote monitoring systems that can warn clinicians about possible complications and track deviations from the usual recovery pattern so that the ‘bad’ results can be avoided by responding quickly. With expanded patient access to healthcare services, the essence of health equity is achieved by closing the delivery gaps that allow patients from all locations to receive care with the same specialization and accuracy.

Challenges and limitations

Despite the great promise of AI and ML technologies for spine care, the spread of these technologies faces multiple adoption challenges and limitations. Experts must address existing problems to facilitate the safe, ethical deployment of AI and ML solutions in clinical settings[110].

Regulatory hurdles and ethical concerns

AI implementation requires strict regulatory supervision in spine care to guarantee patient safeguarding and efficiency. The regulatory processes for AI technology face challenges because their current frameworks cannot adequately match the fast-moving development of AI innovations. The creation of validation studies proving extensive functional testing forms a prerequisite before AI-driven tools receive approval from bodies like the Food and Drug Administration or European Medicines Agency[111].

Besides technical and regulatory hurdles, the spine care sector faces multiple ethical issues when integrating AI solutions. Data privacy problems must be a top priority in maintaining secure practices when handling critical patient data. AI systems in healthcare delivery contain potential bias risks that raise urgent ethical questions regarding equity and fairness. Significant implementation problems arise from the absence of formal AI accountability procedures, making liability assessments for AI-related diagnostic errors and negative health results[112].

Integration challenges with existing healthcare infrastructure

Existing healthcare frameworks face substantial integration challenges when new AI and ML technologies need to become a seamless part of their operations. Many health organizations depend on outdated legacy technology that cannot handle advanced AI applications properly. Implementing AI algorithms within electronic health record systems necessitates substantial technical changes and adherence to interoperability guidelines, presenting expensive and complex operational challenges[113].

To achieve successful integration, clinicians require sufficient training to use AI tools competently. While implementing AI-based solutions, healthcare providers resist changes that, coupled with their lack of AI knowledge, slow adoption. Doctors need straightforward system designs and detailed training to defeat implementation barriers for new technologies to smoothly fit into clinical work processes[114].

CONCLUSION

This review integrates the benefits and difficulties of AI/ML in spine care, paying particular attention to innovative techniques like early illness predictive modeling, individualized rehabilitation, and AI-driven post-op monitoring. In this work, we reached a conclusion focusing on the interdisciplinary integration of AI/ML into spine care and highlighted the need to focus on a unified validation methodology, ethical frameworks, and development for true AI/ML integration into spine care technology. We identified gaps within prior literature reviews that could be optimally advanced, resulting in meaningful impact in the field. Embedding AI and ML software into spine care services brings precision medicine to new heights with comprehensive potential to reform all elements of spine health treatment. Through their roles in diagnosis and treatment planning, these systems deliver revolutionary solutions that address chronic issues encountered by medical professionals and patients. ML and AI systems improve precision while speeding treatment delivery and personalizing care, resulting in spine care that works better for all patients. Medical imaging for spinal examination underwent a significant transformation with AI tools and systems that now automate the detection of conditions, including herniated discs, scoliosis, and other degenerative spinal diseases. Convolutional neural networks within deep learning systems show outstanding results for complex medical image analysis, which leads to decreased human monitoring mistakes and faster early healthcare responses. Technological progress increases diagnosis precision while enabling early intervention to prevent the worsening of spinal conditions and their related problems.