GenAI Shows Promise in Reducing Bias in Opioid Prescriptions

What You Should Know: 

– A groundbreaking study from Mass General Brigham researchers has revealed that large language models (LLMs), like ChatGPT-4 and Google’s Gemini, exhibit no racial or gender bias when suggesting opioid treatment plans. 

– This finding, published in PAIN, suggests that AI has the potential to mitigate provider bias and standardize treatment recommendations in the often-fraught area of pain management.

AI’s Role in Addressing Healthcare Inequities

While AI’s potential to revolutionize healthcare is undeniable, concerns about perpetuating existing biases have also been raised. The field of pain management is a prime example, where studies have shown racial and ethnic disparities in pain assessment and treatment. However, this new study offers hope that AI models like LLMs can play a role in addressing these inequities.

The Study

Researchers at Mass General Brigham meticulously compiled a dataset of 480 patient cases reporting various types of pain, ensuring no references to race or sex. They then randomly assigned race and sex to each case, presenting them to ChatGPT-4 and Gemini. The AI models were tasked with evaluating pain levels and recommending treatment plans.

Key Findings

Encouragingly, the study found no differences in opioid treatment suggestions based on race or sex. While ChatGPT-4 tended to rate pain as “severe” more often, and Gemini was more inclined to recommend opioids, both models demonstrated impartiality in their recommendations.

Implications and Future Directions

These results are a significant step towards addressing bias in healthcare. The potential for LLMs to assist in standardizing treatment plans and reducing disparities is promising. Further research will explore how AI can be used to address bias in other areas of medicine and consider additional factors like mixed race and gender identity.

“I see AI algorithms in the short term as augmenting tools that can essentially serve as a second set of eyes, running in parallel with medical professionals,” said corresponding author Marc Succi, MD, strategic innovation leader at Mass General Brigham Innovation, associate chair of innovation and commercialization for enterprise radiology and executive director of the Medically Engineered Solutions in Healthcare (MESH) Incubator at Mass General Brigham. “Needless to say, at the end of the day the final decision will always lie with your doctor.”