Health inequities, bias, and artificial intelligence

Abstract

Musculoskeletal (MSK) pain leads to significant healthcare utilization, decreased productivity, and disability globally. Due to its complex etiology, MSK pain is often chronic and challenging to manage effectively. Disparities in pain management—influenced by provider implicit biases and patient race, gender, age, and socioeconomic status—contribute to inconsistent outcomes. Interventional radiology (IR) provides innovative solutions for MSK pain through minimally invasive procedures, which can alleviate symptoms and reduce reliance on opioids. However, IR services may be underutilized, especially due to current treatment paradigms, referral patterns, and in areas with limited access to care. Artificial intelligence (AI) presents a promising avenue to address these inequities by analyzing large datasets to identify disparities in pain management, recognizing implicit biases, improving cultural competence, and enhancing pain assessment through multimodal data analysis. Additionally, patients who may benefit from an IR pain procedure for their MSK pain may then receive more information through their providers after being identified as a candidate by AI sifting through the electronic medical record. By leveraging AI, healthcare providers can potentially mitigate their biases while ensuring more equitable pain management and better overall outcomes for patients.