Work no less burdensome for 70% of 276 radiologists using AI
Radiologists across Europe have been finding AI algorithms useful for improving efficiency at some tasks—yet, overall, few have received AI-attributable workload relief.
The findings are from a survey of 690 radiologists from 229 institutions in 32 countries who completed a survey conducted by the European Society of Radiology, of which all respondents were members.
The group’s findings were published June 21 in the ESR journal Insights into Imaging.
Radiologist Christoph Becker, MD, professor emeritus at the University of Geneva and former member of ESR’s executive council, and colleagues focused on answers from respondents who had practical clinical experience with one or more artificial intelligence algorithms.
Of these radiologists, 276 in number, 52% work in academic institutions (n = 143), 37% in regional hospitals and 11% in private practice.
The cohort’s most commonly cited AI use case was Assistance during image interpretation—defined as detecting or marking nodules, emboli and so on. Some 142 respondents (51.5%) checked this off.
Runners-up were Workflow prioritization at 40% and Assistance with image post-processing, 29%.
Also showing were Primary interpretation (essentially replacing the radiologist, 7%), Assistance during interpretation (access to literature, facilitating differential diagnoses, etc., 5%) and Quality control (4%).
Other key findings:
- Of 185 radiologists who used AI-based algorithms for diagnostic purposes, 140 (75.7%) considered the results of the algorithms generally reliable.
- Some 64 radiologists (34.6%) mentioned their use of an algorithm in the corresponding radiology report, while 32 respondents (17.3%) informed patients of the algorithm’s role in their care.
- Just 49 respondents (17.8%) reported technical difficulties with integration of AI-based tools into their workflow.
Arguably the most meaningful finding for radiological AI stakeholders and close watchers was that only 42 respondents (22.7%) said AI implementation had translated into a significant workload reduction.
By comparison, 129 (69.8%) experienced no such salutary effect.
This may help explain why just 13.3% of the full 690-respondent field expressed an intention to acquire AI tools.
Also of interest: Of 111 respondents who used AI-based algorithms for clinical workflow prioritization, 26 (23.4%) considered algorithms to be very helpful for reducing the workload of the medical staff, while the others found them only moderately helpful (62.2%) or not helpful at all (14.4%).
The authors state the results suggest that, compared with initial expectations for AI’s impact as expressed by ESR members in a prior survey,
… the use of AI- powered algorithms in practical clinical radiology today is limited, most importantly because the impact of these tools on the reduction of radiologists’ workload remains unproven. As more experience with AI-powered algorithms for specific scenarios is being gained and some of the barriers to their use may become mitigated in the future, a follow-up to this initial survey could provide further insights into the usefulness of these tools.”
The ESR invited all 27,700 member radiologists to participate in the survey by email in early 2022.
The journal has posted the study in full for free.
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Reference:
- European Society of Radiology: “Current practical experience with artificial intelligence in clinical radiology: A survey of the European Society of Radiology.” Insights into Imaging, June 21, 2022. DOI: https://doi.org/10.1186/s13244-022-01247-y
Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.