Case Study in Data Science

A Qualitative Study to Understand Patient Perspective on the Use of Artificial Intelligence in Radiology

Description of the Problem: Neglect of Patient’s Perspective on Artificial Intelligence Use in Radiology

Artificial intelligence (AI) refers to the development of computer algorithms to accomplish tasks traditionally associated with human intelligence, such as the ability to learn and solve problems [1]. Currently, several narrow task-specific AI applications have been shown to match and occasionally surpass human intelligence [2]. However, it is expected that general AI will surpass human performance in specific applications in the coming years [2].

In radiology, AI can provide detailed

What Was Done: Setting Up a Qualitative Study to Understand the Patient’s View on the Use of AI in Radiology

Before developing and implementing an AI system for a particular radiological task, it would be very useful to understand how patients view the use of AI in radiology. The goal of this qualitative study is to develop an understanding of the patient’s level of knowledge of AI and to explore the meanings patients ascribe to key topics, such as radiology and AI. Finally, we aim to identify domains related to patients’ perspective on the use of AI in radiology. This prospective study was approved

Outcome: Six Key Domains Related to AI Use in Radiology

A prominent result from the interviews is that patients’ views on radiology are diverse and sometimes incorrect. For instance, to patients it is not clear what the differences are in roles and responsibilities of different staff members (radiologist, radiologic technician, nurse, or doctor’s assistant) at the radiology department. With respect to AI in general, patients noted either no particular associations or mentioned factors like “loss of jobs,” “making life easier,” or “what need do we

Conclusions

The six identified domains of patients’ perspective on the use of AI in radiology could provide a framework for patient education and for future quantitative research to investigate and match patients’ expectations with the development and implementation of AI systems in radiology practice.