Design of a Novel Umbilical Venous Catheter with Echogenic Distance Markers for Increased Placement Accuracy; Examining the Impact of Artificial Intelligence on Healthcare Disparities: How Encoded Bias Can Reinforce Existing Inequities

Developments in healthcare technology have transformed the medical landscape, providing innovative methods to address illnesses and improve the overall wellbeing of humanity. My technical report explores methods of incorporating distance markers into umbilical venous catheters (UVCs) to ameliorate ultrasound visibility, shifting the current standard towards point-of-care ultrasound (POCUS) and limiting the need for X-ray visualization. This will improve accuracy during placement and reduce complications associated with improper localization of the catheter tip while enhancing patient care and increasing physician confidence. My STS research paper focuses on examining the impact of Artificial Intelligence (AI) on healthcare disparities, specifically looking at how encoded bias can reinforce existing inequities. AI-based technologies are quickly becoming a permanent fixture in today’s society, but these technologies have been shown to exhibit an array of different forms of bias. The integration of biased AI into healthcare has the capacity to exacerbate pre-existing social inequalities in medicine, which can become detrimental to their health. In both scenarios, the translation of technological improvements into clinical practices can have mixed implications; new innovations can increase efficiency and improve patient outcomes, but they do so at the risk of harming minority groups, especially those that already face marginalization within society.

School of Engineering and Applied Science

Bachelor of Science in Biomedical Engineering

Technical Advisors: David Kaufman, Nicholas Heitkamp

STS Advisor: Pedro A. P. Francisco

Technical Team Members: Allison Martens