Considerations for Implementing AI Decision Aids in Radiation Oncology

Overview:

The application of AI technologies in the healthcare sector has been of great interest in recent years. There have been significant investments made into developing these technologies (Murphy, 2017; Silcoff, 2018; KPMG, 2018), and AI in healthcare has been the topic of numerous conferences (O’Neil, 2017), editorial commentaries (Beam & Kohane, 2016; Norrie, 2018; Naylor, 2018; Hinton, 2018), and news blogs (Siwicki, 2017; Siwicki, 2019; Towers-Clark, 2018; HIMSS, 2018). Of the many possible applications, the use of ML technology to generate predictions to inform medical decision-making has incited discussion of the implications on the health system (Kuziemsky, 2016; Shahid et al., 2019). Extensive research and development has been conducted for AI technology for healthcare, however, there remain few examples of successful AI implemented in health settings (Lancet, 2018).

As technology advances and the use of artificial intelligence (AI) technology is adopted in various fields, there are increasing efforts to develop AI technology for healthcare applications to improve care delivery and patient outcomes. However, there are few examples of AI technology currently being used in clinical settings. Thus in the formation of this project, the team wanted to understand why this disparity exists.

The objective of this project is to explore the challenges and considerations for implementing AI predictive technologies in clinical settings.

Capstone Team: 

Paige Gilbank
Kaleigh Johnson-Cover
Tran Truong

 

Capstone Advisory Committee:

Jennifer Bell
Michael Carter
Greg Jamieson
Jennifer Jones

 

TRP Faculty Lead:

Adriana Ieraci