PainRelief.com Interview with:
Marc D. Succi, MD
Strategic Innovation Leader | Mass General Brigham Innovation
Associate Chair of Innovation & Commercialization | Mass General Brigham Enterprise Radiology
Co-Director, Innovator Growth Division, Mass General Brigham Innovation
Attending Radiologist | Mass General Emergency Radiology 
Assistant Professor of Radiology | Harvard Medical School
Executive Director, Mass General Brigham MESH Incubator

PainRelief.com: What is the background for this study?

Response: This study investigates whether large language models (LLMs), such as GPT-4 and Google’s Gemini, introduce racial, ethnic, or sex-based bias when recommending opioid treatments for pain management. Existing literature highlights racial disparities in pain treatment, with Black patients often receiving less aggressive pain management compared to White patients.

LLMs, as AI tools trained on large datasets, may either perpetuate these biases or help standardize treatment across diverse patient groups. This study analyzed hundreds of real-world patient cases, representing various pain conditions, to assess if race, ethnicity, or sex influenced the LLMs’ opioid treatment recommendations.