The European Society of Radiology just published new insights into how imaging AI is being used across Europe and how the region’s radiologists view this emerging technology.
The Survey – The ESR reached out to 27,700 European radiologists in January 2022 with a survey regarding their experiences and perspectives on imaging AI, receiving responses from just 690 rads.
Early Adopters – 276 the 690 respondents (40%) had clinical experience using imaging AI, with the majority of these AI users:
- Working at academic and regional hospitals (52% & 37% – only 11% at practices)
- Leveraging AI for interpretation support, case prioritization, and post-processing (51.5%, 40%, 28.6%)
AI Experiences – The radiologists who do use AI revealed a mix of positive and negative experiences:
- Most found diagnostic AI’s output reliable (75.7%)
- Few experienced technical difficulties integrating AI into their workflow (17.8%)
- The majority found AI prioritization tools to be “very helpful” or “moderately helpful” for reducing staff workload (23.4% & 62.2%)
- However, far fewer reported that diagnostic AI tools reduced staff workload (22.7% Yes, 69.8% No)
Adoption Barriers – Most coverage of this study will likely focus on the fact that only 92 of the surveyed rads (13.3%) plan to acquire AI in the future, while 363 don’t intend to acquire AI (52.6%). The radiologists who don’t plan to adopt AI (including those who’ve never used AI) based their opinions on:
- AI’s lack of added value (44.4%)
- AI not performing as well as advertised (26.4%)
- AI adding too much work (22.9%)
- And “no reason” (6.3%)
US Context – These results are in the same ballpark as the ACR’s 2020 US-based survey (33.5% using AI, only 20% of non-users planned to adopt within 5 years), although 2020 feels like a long time ago.
Even if this ESR survey might leave you asking more questions (What about AI’s impact on patient care? How often is AI actually being used? How do opinions differ between AI users and non-users?), more than anything it confirms what many of us already know… We’re still very early in AI’s evolution, and there’s still plenty of performance and perception barriers that AI has to overcome.