Voices in Pain Medicine: Harnessing Artificial Intelligence in Interventional Pain Management

Artificial intelligence (AI) is rapidly transforming the landscape of healthcare, and interventional pain management is no different. Interventional pain management comprises multiple domains, which could be impacted by AI, including imaging interpretation, outcome prediction, and documentation. As AI systems become increasingly sophisticated, integration seems almost inevitable. However, this integration with AI introduces new questions regarding safety, ethics, and workflow efficiency. Understanding current AI research and where it is headed is helpful for clinicians looking to improve clinical and procedural practice.

Firstly, it is important to understand the basics of AI. AI functions by identifying patterns within large datasets and using those patterns to make predictions, classifications, or decisions. Most current AI uses machine learning, in which algorithms improve their performance by iteratively learning from examples rather than following fixed, rule-based instructions. Building on this, deep learning uses multi-layered neural networks to uncover complex, non-linear relationships in data, enabling AI to detect patterns that are often imperceptible to humans. Within deep learning, convolutional neural networks (CNNs) are specifically designed for image analysis, using filters that scan across an image to identify features such as edges, textures, or anatomical structures. By stacking these layers, CNNs progressively refine their understanding of visual information, allowing highly accurate recognition and segmentation[1].

Currently, one of the most looked at applications of AI in interventional pain lies in image interpretation and procedural guidance. These systems commonly rely on CNNs, which excel at analyzing medical images. In ultrasound-guided procedures, for example, CNNs have been successfully used for real-time needle detection and tip localization [2]. Similar models are being explored for real-time fluoroscopic interpretation, where AI can assess dye spread patterns, flag suboptimal views, or suggest repositioning to optimize precision [3-4]. Additionally, Luchmann et al. introduced an AI-based technique capable of generating accurate, real-time 3D spinal reconstructions from only a few fluoroscopic images, offering a potential pathway toward more sophisticated procedural navigation[5]. In spinal imaging more broadly, deep learning models have been shown to reduce noise and enhance image quality in MRI and CT; for example, Bash et al. demonstrated that AI-driven reconstruction allows spinal MR imaging to be acquired 40% faster while maintaining or exceeding standard-of-care quality[6].

AI is also emerging as a promising tool for risk stratification and outcome prediction. Interventional decisions depend on a combination of clinical experience, imaging findings, and published success rates. In a world of large datasets drawn from electronic medical records, imaging repositories, and procedural registries, the learning opportunities for predictive AI models are plentiful and are beginning to offer more objective predictions. For instance, in a multicenter study by Ounajim et al., several machine learning models were developed and validated using pain outcomes at one year in 103 patients undergoing spinal cord stimulation (SCS). The study found that almost all machine learning models provided superior predictive accuracy compared to the conventional lead trial. Among the models, regularized logistic regression offered the best balance between predictive performance and interpretability[7]. Another study by Lee et al. developed machine learning models, which combined spinal imaging radiomics (computational algorithms that extract large numbers of quantitative metrics from medical images) with clinical data to predict SCS response. Using the largest US SCS database, the models achieved 90% accuracy for predicting both 50% and 70% pain relief responders, with AUCs of 91.4% and 86.1%, respectively [8]. Lastly, Gopal et al. explored whether intraoperative EEG features, combined with machine-learning techniques, can predict which patients will respond to SCS. The authors analyzed intraoperative EEG from patients undergoing SCS implantation and identified EEG features, particularly the alpha-theta peak power ratio in somatosensory and temporal regions, that reliably distinguished responders from non-responders. By integrating these EEG features with clinical and patient-reported measures, they trained a machine-learning model that predicted treatment response with approximately 88% accuracy[9].

Beyond imaging and prediction, an under-recognized, arguably more impactful role of AI is in workflow optimization and documentation. Interventional pain practices face heavy administrative burdens, including prior authorizations, procedural documentation, and the management of large volumes of clinical notes. Natural language processing systems can help summarize patient histories, extract relevant pain descriptors, or streamline procedural documentation, allowing clinicians to focus more energy on patient care[4]. These tools tackle the real bottlenecks in clinical practice and ultimately have a significant impact on clinic efficiency and patient experience.

With these opportunities come equally important challenges. For one, machine learning and AI more generally are only as reliable as the data they are trained on, and interventional pain datasets remain relatively small, heterogeneous, and vulnerable to sampling bias. Reviews of AI in radiology and procedural guidance have shown that models trained on narrow datasets often perform poorly when exposed to new populations or different imaging hardware[3]. Bias and limited generalizability, therefore, remain real concerns, particularly if algorithms developed within a single institution, vendor, or demographic group are deployed widely without recalibration. Regarding workflow and documentation, even highly accurate AI models may fail in clinical practice if they slow down clinic visits and notes or if they increase cognitive load or require specialized equipment not available in all settings. Lastly, ethical and regulatory considerations are top of mind as AI comes into sharper focus and begins to influence procedural decisions. These considerations include questions of patient transparency, data ownership, informed consent, and liability in the event of AI-related error[3-4,10]. As with any powerful technology, careful governance is essential to ensuring safe, equitable, and meaningful integration into patient care.

With all this said, AI and machine learning will continue to be present in discussions of the future of pain medicine. The key will focus on and inform adoption. This starts with targeting applications that solve real problems, contributing to high-quality registries and research, and maintaining clinical judgment as the backbone of procedural decision-making. The future for trainees and early-career clinicians will require literacy in AI-supported tools, both to maximize their benefits and to recognize their limitations. For established, the coming decade will likely bring gradual, but meaningful shifts toward more data-driven procedural planning, enhanced imaging support, and predictive follow-up. AI will not replace the nuance, judgment, and hands-on skill that define the specialty, but it will hopefully enhance safe, efficient, and personalized pain interventions. By engaging early, asking critical questions, and advocating for patient-centered implementation, pain physicians can help ensure that AI becomes a tool that strengthens our practice and improves the lives of the patients we serve.

Matthew Gabriel, MD
AAPM Resident Ambassador

Resident Physician, PGY-2

Physical Medicine & Rehabilitation, Northwestern Medicine Marianjoy

References

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