Health inequities, bias, and artificial intelligence

Area of Science:

• Medical Imaging and Interventional Radiology
• Health Equity and Artificial Intelligence

Background:

• Musculoskeletal (MSK) pain is a global health issue, often chronic and difficult to manage effectively.
• Disparities in MSK pain management exist, influenced by provider biases and patient demographics, leading to inconsistent outcomes.
• Interventional radiology (IR) offers minimally invasive solutions for MSK pain but may be underutilized due to systemic factors.

Purpose of the Study:

• To explore the potential of Artificial Intelligence (AI) in addressing inequities in MSK pain management.
• To investigate how AI can identify and mitigate provider biases and improve cultural competence in pain care.
• To enhance pain assessment and patient identification for IR procedures through AI analysis of electronic medical records.

Main Methods:

• AI algorithms analyze large datasets to identify disparities in MSK pain management.
• AI tools are proposed to recognize implicit biases and improve cultural competence among healthcare providers.
• AI facilitates multimodal data analysis for enhanced pain assessment and patient stratification for IR procedures.

Main Results:

• AI can identify disparities in pain management influenced by patient and provider factors.
• AI has the potential to improve cultural competence and mitigate provider biases.
• AI can identify patients who may benefit from interventional radiology (IR) pain procedures.

Conclusions:

• AI offers a promising approach to mitigate biases and ensure more equitable MSK pain management.
• Leveraging AI can lead to better identification of patients eligible for IR pain interventions.
• AI can contribute to improved overall patient outcomes in managing chronic MSK pain.