AI Biases in Healthcare

I have very little personal experience with AI, so I decided for this module to gain some base knowledge on how AI is used in a healthcare setting, and what some of the repercussions of that usage could be.

The results of my online searching had a recurring theme: AI and race/gender biases. I was only vaguely aware of this being an issue, so I decided that I would focus this module’s self-study on the implications of these biases in patient care.

Here’s an overview of what I learned:

Instructions: Click on the play button in the center of the presentation to start, then use < > buttons in bottom center to navigate.

I’m still figuring out how to apply this learning in my role as an instructional designer, as I don’t work directly in patient care. However, knowledge can drive positive change in any context. I hope that by integrating this understanding into the training materials I develop, I can provide our department’s social workers with additional tools to advocate effectively for their patients.

Sources:

Ziad Obermeyer et al, Dissecting racial bias in an algorithm used to manage the health of populations.Science366,447-453(2019).DOI:10.1126/science.aax2342

Noor, P. (2020/02/12/). Can we trust AI not to further embed racial bias and prejudice? BMJ : British Medical Journal (Online), 368 doi:https://doi.org/10.1136/bmj.m363

AMA J Ethics. 2019;21(2):E167-179. doi: 10.1001/amajethics.2019.167.

List, J. M., Palevsky, P., Tamang, S., Crowley, S., Au, D., Yarbrough, W. C., Navathe, A. S., Kreisler, C., Parikh, R. B., Wang-Rodriguez, J., Klutts, J. S., Conlin, P., Pogach, L., Meerwijk, E., & Moy, E. (2023). Eliminating Algorithmic Racial Bias in Clinical Decision Support Algorithms: Use Cases from the Veterans Health Administration. Health equity, 7(1), 809–816. https://doi.org/10.1089/heq.2023.0037