Abstract
Navigating complex healthcare and institutional systems requires an intentional balance between technological precision and human-centric design. This work presents two parallel projects that examine how specialized technological interventions can overcome systemic barriers to equity and safety. The first project, WahooWay, is a dynamic, accessibility-centered mobile navigation application developed to improve mobility across the University of Virginia’s Grounds. Motivated by the inadequacy of static, incomplete maps that often exclude essential building-level details, WahooWay provides personalized, real-time route planning tailored to individual mobility levels. By prioritizing step-free paths, ramps, and accessible entrances while integrating user-reported hazard alerts, such as broken elevators or construction, the system ensures that navigation reflects actual campus conditions rather than static assumptions.
Complementing this technical solution is a sociotechnical research paper investigating the integration of Artificial Intelligence (AI) and radiomics into oncological diagnostic workflows. This research explores the transition from qualitative visual inspection to quantitative feature extraction, emphasizing how AI can mitigate inherent human cognitive failures such as "satisfaction of search" or "under-reading." Much like the implementation gap found in campus navigation tools, this research identifies a critical divide between the mathematical optimization of AI models in controlled labs and their practical utility in high-stakes clinical environments. It argues that while AI can significantly enhance diagnostic precision, its successful integration is contingent upon a radiologist-in-the-loop model that preserves human diagnostic authority and professional intuition.
Together, these projects demonstrate how targeted technological tools can transform the human experience by promoting independence and informed decision-making. WahooWay advances UVA’s commitment to equitable access by providing a scalable foundation for inclusive wayfinding. Simultaneously, the STS research underscores that as AI becomes more embedded in healthcare, its primary role must be to augment, rather than replace, the clinical judgment and compassionate oversight essential to medicine. Both works conclude that for technology to be truly effective, it must be responsive, context-aware, and developed through a bidirectional exchange of expertise between technical engineers and the end-users who navigate these systems daily.
