The article provides an overview of artificial intelligence (AI) and its potential applications in pain medicine. AI, which has evolved significantly over the past 60 years, is now being increasingly used in healthcare to improve disease recognition, treatment optimization, cost savings, product development, decision-making, and marketing. In the context of pain medicine, AI can enhance disease recognition and treatment selection. The authors define key terms such as AI, big data, machine learning (ML), and deep learning (DL), and discuss the platforms and technologies used to store, manage, and analyze healthcare data, including electronic health records (EHRs), health information exchanges (HIEs), cloud computing, and the Internet of Things (IoT) devices. The article highlights several clinical applications of AI in pain medicine, such as opioid monitoring and risk reduction, accessibility and outcome optimization, neuromodulation device optimization, and image optimization. AI can help identify patterns and risk factors associated with chronic pain, optimize treatment strategies, and improve patient outcomes. For example, AI can analyze patient data to predict treatment outcomes, optimize spinal cord stimulation parameters, and enhance image quality in pain procedures. However, the article also addresses challenges and ethical considerations, such as the need for robust ethical frameworks, the potential for AI to exacerbate healthcare inequities, and the importance of protecting patient privacy. The authors emphasize the importance of pain medicine physicians staying informed about AI to prepare for its clinical adoption and to ensure that AI benefits both patients and healthcare providers.The article provides an overview of artificial intelligence (AI) and its potential applications in pain medicine. AI, which has evolved significantly over the past 60 years, is now being increasingly used in healthcare to improve disease recognition, treatment optimization, cost savings, product development, decision-making, and marketing. In the context of pain medicine, AI can enhance disease recognition and treatment selection. The authors define key terms such as AI, big data, machine learning (ML), and deep learning (DL), and discuss the platforms and technologies used to store, manage, and analyze healthcare data, including electronic health records (EHRs), health information exchanges (HIEs), cloud computing, and the Internet of Things (IoT) devices. The article highlights several clinical applications of AI in pain medicine, such as opioid monitoring and risk reduction, accessibility and outcome optimization, neuromodulation device optimization, and image optimization. AI can help identify patterns and risk factors associated with chronic pain, optimize treatment strategies, and improve patient outcomes. For example, AI can analyze patient data to predict treatment outcomes, optimize spinal cord stimulation parameters, and enhance image quality in pain procedures. However, the article also addresses challenges and ethical considerations, such as the need for robust ethical frameworks, the potential for AI to exacerbate healthcare inequities, and the importance of protecting patient privacy. The authors emphasize the importance of pain medicine physicians staying informed about AI to prepare for its clinical adoption and to ensure that AI benefits both patients and healthcare providers.