Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.

Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.

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Description
Rapid advancements in Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are widening the playing field for automated decision assistants in healthcare. The field of radiology offers a unique platform for this technology due to its repetitive work structure, ability to leverage large data sets, and high position for

Rapid advancements in Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are widening the playing field for automated decision assistants in healthcare. The field of radiology offers a unique platform for this technology due to its repetitive work structure, ability to leverage large data sets, and high position for clinical and social impact. Several technologies in cancer screening, such as Computer Aided Detection (CAD), have broken the barrier of research into reality through successful outcomes with patient data (Morton, Whaley, Brandt, & Amrami, 2006; Patel et al, 2018). Technologies, such as the IBM Medical Sieve, are growing excitement with the potential for increased impact through the addition of medical record information ("Medical Sieve Radiology Grand Challenge", 2018). As the capabilities of automation increase and become a part of expert-decision-making jobs, however, the careful consideration of its integration into human systems is often overlooked. This paper aims to identify how healthcare professionals and system engineers implementing and interacting with automated decision-making aids in Radiology should take bureaucratic, legal, professional, and political accountability concerns into consideration. This Accountability Framework is modeled after Romzek and Dubnick’s (1987) public administration framework and expanded on through an analysis of literature on accountability definitions and examples in military, healthcare, and research sectors. A cohesive understanding of this framework and the human concerns it raises helps drive the questions that, if fully addressed, create the potential for a successful integration and adoption of AI in radiology and ultimately the care environment.
ContributorsGilmore, Emily Anne (Author) / Chiou, Erin (Thesis director) / Wu, Teresa (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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Description
The premise of this thesis developed from my personal interests and undergraduate educational experiences in both industrial engineering and design studies, particularly those related to product design. My education has stressed the differences in the ways that engineers and designers approach problem solving and creating solutions, but I am most

The premise of this thesis developed from my personal interests and undergraduate educational experiences in both industrial engineering and design studies, particularly those related to product design. My education has stressed the differences in the ways that engineers and designers approach problem solving and creating solutions, but I am most interested in marrying the two mindsets of designers and engineers to better solve problems creatively and efficiently.
This thesis focuses on the recent appearance of generative design technology into the world of industrial design and engineering as it relates to product development. An introduction to generative design discusses the uses and benefits of this tool for both designers and engineers and also addresses the challenges of this technology. The relevance of generative design to the world of product development is discussed as well as the implications of how this technology will change the roles of designers and engineers, and especially their traditional design processes. The remainder of this paper is divided into two elements. The first serves as documentation of my own exploration of using generative design software to solve a product design challenge and my reflections on the benefits and challenges of using this tool. The second element addresses the need for employing quantitiative methodologies within the generative design process to aid designers in selecting the most advantageous design option when presented with generative outcomes. Both sections aim to provide more context to this new design process and seek to answer questions about some of the ambiguous processes of generative design.
ContributorsElgin, Mariah Crystal (Author) / Bacalzo, Dean (Thesis director) / Gel, Esma (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Dean, Herberger Institute for Design and the Arts (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05