Creating Equitable AI Models for Multiple Myeloma

The International Myeloma Society's 21st annual conference is set to take place in Rio de Janeiro, Brazil, from September 25 to 27. This gathering will bring together leading experts in the field to discuss the latest advancements in myeloma research, covering fundamental, preclinical, and clinical aspects of the disease.

Anant Madabhushi, PhD, executive director for the Emory Empathetic AI for Health Institute, proposes using AI to address disparities in multiple myeloma care. He argues that systemic racism contributes significantly to these disparities, but there may also be subtle phenotypic differences between populations that can be identified to develop more tailored treatment approaches.

This transcript has been lightly edited for clarity.

Transcript

How can AI help address disparities in multiple myeloma care and outcomes?

I will react by saying that the issue of disparities in cancer care is well established. We know that there are several different reasons for the disparities that we observe across different populations in the context of cancer care. Let's call out the 800-pound gorilla in the room. A lot of it has been the issue of systemic racism that has caused significant disparities in access and subsequently outcomes for different populations, particularly populations of color. As we look to better understand the disease, and we're seeing this in other cancers and other diseases, that beyond the social determinants of health and beyond the socioeconomic factors and access, we also are starting to acknowledge that there are subtle differences in the phenotype of the disease across different populations. And our work in prostate cancer and endometrial cancer has shown that using technologies like AI, we can start to tease out very subtle differences in the appearance of the disease across different populations. For instance, in prostate cancer and endometrial cancer, our work has revealed that there are cellular-level differences in the pathology images of, say, Black patients and White patients in the context of endometrial cancer and prostate cancer.

Again, I will repeat what I initially said. I know very little about multiple myeloma and I'm looking to learn at this particular meeting but I think that there's an opportunity to really use powerful technologies like AI to start to dive into understanding phenotypic differences between populations. I'm not suggesting that there are necessarily differences, but if there are differences, then we need to be cognizant of those differences and try to start accounting for that, potentially in starting to create more tailored AI models.

One of the things that AI has been accused of, quite rightly, is the fact that it is very prone to bias. It is very prone to exacerbating inequity and the only way to really address that is to be intentional about the way we develop the AI. To do that, we have to look at these approaches to try to understand, first and foremost, are there differences in the appearance of the disease across different populations? If there are, let's celebrate those differences and account for those differences in creating a more unbiased, more equitable AI model, or if need be, create a more population specific AI model that is more geared and catered to specific populations.