Potential aplications AI in the field of pain medicine
Mil CHeolChang
AI has numerous potential applications in the field of pain medicine. First, AI has the potential to guide the selection of diagnostic tools that identify the origins of pain, enhancing diagnostic accuracy. When a patient reports pain in a specific region, physicians must consider multiple possible causes, narrowing down potential conditions based on the patient’s clinical profile and physical examination results. Subsequently, the root cause of the pain can be identified through additional diagnostic measures, encompassing imaging, neurophysiological assessments, and diagnostic blocks. Throughout this procedure, physicians must consider potential disorders inducing pain from a multitude of sources, such as the spinal nerves, peripheral nerves, muscles, tendons, ligaments, joints, and bones. However, as the different medical fields become more specialized, there is an increasing risk of knowledge gaps. For example, neurologists may not be fully acquainted with musculoskeletal issues, and vice versa. This can result in misdiagnosis due to a limited perspective based solely on the attending doctor’s specialized training, even though a comprehensive consideration across both neural and musculoskeletal diseases is required. Based on clinical big data, AI diagnostic algorithms can be developed using patient demographics, symptomatology, test outcomes, and prior diagnoses. AI algorithms developed in this manner can be trained to automatically suggest necessary tests when patient symptoms are input, and based on test results, advise physicians on likely sources of pain.
Second, AI can assist in image interpretation. Imaging tests, pivotal for diagnosing neural and musculoskeletal disorders, are heavily relied upon by clinicians. However, clinicians not specializing in radiology often experience difficulty interpreting these images. One of the most salient advantages of using AI for data analysis lies in its ability to analyze image data and identify essential features. A surge in the number of AI algorithms, crafted for the automatic diagnosis of various neuromuscular skeletal images, radiographs, computed tomography images, and magnetic resonance imaging scans, has emerged recently. However, in real-world clinical scenarios, only those AI algorithms focused on bone fracture detection are in active use. A significant portion of these algorithms have diagnostic accuracy rates below 90%, which is suboptimal for clinical use[1-3]. Nonetheless, with the continued accumulation of image data from numerous clinics and hospitals, the accuracy of these algorithms is expected to keep improving over time. In the future, AI algorithms are expected to help doctors interpret neuromuscular skeletal disorders and explain the imaging results to patients. Furthermore, the use of automatic image interpretation by AI algorithms could also reduce consultation times.
Third, AI can be useful in predicting pain treatment prognosis. The ability to predict a patient’s response after pain treatment is integral to establishing an effective treatment plan. Numerous previous studies that used a traditional statistical analysis have delved into understanding treatment responses based on the specific type and severity of conditions. However, these studies typically highlight trends within larger patient groups, falling short of predicting individual treatment responses. However, when fed with individual data, AI algorithms can predict the individual patient’s treatment response based on the corresponding output[4,5]. Such AI-driven prognostic predictions can play a pivotal role in crafting tailored pain management strategies, potentially optimizing treatment outcomes. However, for a tangible implementation in clinical settings, it is essential to augment the prediction accuracy of these AI algorithms by training them with more data.
The Future Pain Medicine Clinic
A potential workflow of a future pain medicine clinic is provided in This is hypothetical at this point, but platforms are being created to more seamlessly provide efficient healthcare and improve outcomes. This potential workflow highlights those aspects.
Potential workflow of a future pain medicine clinic.
Challenges
Machine learning and deep learning occur with large data. In social media websites, millions of users generate big data from multiple sources every hour. Through pattern recognition, trends and new directions for movement are identified with curated information provided to individual users. For SCS, data from external stimuli and social interaction can calibrate a device for individual users. However, unlike computers and phones, available to billions, SCS device prevalence is limited. At best thousands of spinal cord stimulator implants are implanted nationally. Individual stimulator data could be enhanced if a central monitoring device collectively gathers information from other stimulator devices. However, sensitive patient information with collected data could be a breach of privacy. A central device could violate patient confidentiality and privacy rights needing permission before implementing. The Health Insurance Portability and Accountability Act (HIPAA) laws from the 1996 US federal legislation defined these standards and will appraise any incorporation of AI technology.28 Information transfer would need to be encrypted from the source point to the final point, including security audits and access control. Responsible use of AI in pain medicine means ensuring that existing pain diagnosis and treatment disparities falling along racial, ethnic, and gender lines are not further exacerbated.29
AI data conclusions could be disruptive where industry collaboration would mean boldness risking device failure and potential emergence of superior devices. Measuring stimulation differences between devices could mean overriding proprietary claims of each stimulator device. If comparison between companies is challenging comparison within company devices should be feasible such as comparing device stimulation parameters to traditional tonic stimulation. Most systems can revert to traditional stimulation if burst or high-frequency stimulation fails. AI features including pattern recognition, feature extraction and dimensionality reduction can compare settings to extrapolate success or failure of new devices settings to other devices or its own.
Pain and function are common end points to measure. Objective measures, such as function, are easier to measure whereas subjective measures, such as pain, can be challenging. For example, function impairments are common in Parkinson’s patient with tremor and rigidity. These patients are unable to initiate and maintain steady movements. AI and machine learning could correct and calibrate device function to improve normal movement. On the contrary, pain is difficult to calibrate and objectifying pain has been futile. In 1996 the American Pain Society attempted to objectify pain with the pain scale.30 However, the multi-dimensionality of pain, its various pathways, and models of pain processing unclear, a pain scale alone is rudimentary where multiple variables will help construct algorithms for the pain experience. Next pattern generation would create new layers of machine learning improving pain understanding and device development. It is also likely data shift will occur in these scenarios involving multiple variables eventually needing recalibration through external input.
Conclusion
AI has the potential to greatly enhance the field of healthcare by improving diagnostic accuracy, reducing workload on healthcare providers, reducing costs, and enabling personalized treatment plans. It accomplishes this by streamlining workflow, automating tasks, assisting with diagnosis and treatment optimization, and reducing errors. However, there are also challenges that must be addressed, such as the need for robust ethical frameworks and the potential for AI to exacerbate existing healthcare inequities. As research and development in this area continues, it is important to carefully consider these issues and work towards solutions that can maximize the benefits of AI for patients and healthcare providers alike. Regardless, it is imperative that pain medicine physicians educate themselves on AI and ready their practices for clinical adoption.