When asking participants about the effectiveness of different technologies, most participants rated each technology as roughly 6/10

. There were individual scores that placed quite highly but every prompt averaged out to a mean between 6 and 6.5. This implies that, in general, participants gave a middle-of-the-road score and appeared hesitant to display any strong feelings on the topic .

Regarding examples of machine learning and automation that participants experiences regularly, the key response across the cohort was that digital assistants such as Alexa or Google Home is the most prevelant piece of machine learning technology in their lives. Similarly the most common example of automation was self-service kiosks at supermarkets or fast food resturants. This indicates that participants do not see the examples of machine learning and automation in their professional spheres as quite as prevelant as the ones in their personal and leisure spheres, regardless of the percieved importance of radiological and medical technology.

When discussing their positive and negative experiences with technology, an interesting trend emerged - positive experiences tended to be with more personal applications, whereas negative examples were related to more professional systems. Examples that were profoundly negative mostly involved data entry, from recording data about patients on wards to e-prescribing, whereas On the positive end, multiple participants named messaging applications such as WhatsApp and online video conferencing systems like Zoom as examples of technology that have had a positive impact on their lives through the pandemic; one participant explained that these enabled communication with both family and coworkers in a much more fluent way.

The most prevalent view that was expressed when discussing the impact that ML and AI systems would have on participant's radiology career was that it was not going to replace them. Almost all participants explicitly made it clear that they recognised that machine learning systems and technology are tools that are to improve the quality of care that they can provide to patients. However, another point that was raised was the apparent lack of confidence in these ML systems that participants had, as evidenced in the scoring exercise. These results seem to juxtapose with the participant's views in the impact discussion - the optimism regarding machine learning being a large part of their future careers as radiologists does not seem to echo their doubts that these systems can operate to a high standard consistently.