AI-Generated Images Exacerbate Health Care Disparities, Study Finds
A new study suggests that artificial intelligence (AI) tools may be perpetuating existing health care disparities rather than addressing them. Researchers have been evaluating the performance of AI systems across various patient populations to determine whether they can provide equitable care.
The issue is not just about accuracy; it’s also about fairness and bias in decision-making processes. Dr. Roshanak Daneshjou, a clinical dermatologist at Stanford University, has dedicated her work to studying how AI tools perform equitably across different patient groups. Her research focuses on both imaging-based systems and large language models.
Daneshjou’s studies have consistently shown that AI tools often reinforce existing inequities within health care rather than correcting them. This is particularly concerning when it comes to resource allocation, as seen in a widely cited study published in Science. The analysis found that Black patients needed to be significantly sicker than White patients to receive the same level of allocated resources.
The algorithm used in this study relied on health care spending as a proxy for illness severity. However, this measure is flawed because it doesn’t account for systemic barriers faced by Black patients due to socioeconomic status and access to housing. As a result, these patients often spend less on health care despite being sicker.
Daneshjou also referenced her own research evaluating dermatology-focused AI algorithms designed to detect skin cancer. Although none of the algorithms studied were in clinical use at the time, they were being considered for future deployment. The results showed that these systems performed significantly worse at identifying skin cancer on brown and Black skin compared with White skin.
This raises concerns about scaling such technology without addressing performance gaps across different skin tones. It’s essential to recognize that AI-generated images can be used in medical education, but it’s crucial to ensure they accurately represent diverse patient populations.