Radiology is undergoing a significant transformation as it adapts to increasing demands, limited resources, and a workforce stretched thin.
Every radiologist reading a mammogram knows how challenging it is to balance a relentless workload with the tremendous responsibility associated with finding breast cancer as early as possible. Increasing patient volume, a shortage of staff, and image quality can affect workflow, adding to the stress of an already demanding task. Furthermore, variability in skills, training and environment can affect performance, which may lead to detecting cancers later, when they are often harder to treat.
Studies have shown that breast radiologists detect more cancers and more early-stage cancers and have lower recall rates than general radiologists. However, breast imaging has unique stressors compared to other areas of radiology that can contribute to radiologist fatigue and error: the job requires prolonged concentration, comprehensive image review, high volumes of image and data loads, demands to read quickly, and reporting using 3D visualization of complex anatomy. Together, these factors contribute to physical and cognitive fatigue.[1]
As a result, it’s no surprise that breast imaging is in high demand – demand that places a heavy burden on breast radiologists. A recent survey of members of the Society of Breast Imaging (SBI) demonstrated a high prevalence of burnout (77%) affecting practicing breast radiologists in the U.S.[2] Physician burnout, a state of chronic physical and emotional exhaustion due to prolonged stress and heavy workloads, has been increasingly recognized over the past decade as an epidemic within the United States.[3]
Using AI to Improve Workflow in Breast Imaging
A recent article in the JACR notes that one solution to address radiologist staffing and burnout issues is to employ AI tools to improve workflow for interpretative tasks: “The integration of AI into imaging workflows may help to lessen the clinical overload that many radiologists currently face. With computer-aided detection well integrated into radiology practice, AI may also be able to increase efficiency of interpretation by rapidly distinguishing between normal and abnormal findings, ultimately eliminating the need for a radiologist to review at least some of the imaging studies.”
The application of AI to image interpretation tasks such as cancer detection and risk assessment can standardize the tools and approaches to perform breast imaging tasks, particularly reading mammograms. This can decrease the time needed to read exams, improve accuracy, and decrease radiologists’ workload.
By reducing time required to review low risk exams, Transpara reduces the cognitive burden that can contribute to physical and cognitive fatigue. In addition to increasing overall efficiency, the ability to stratify complex, high-risk cases allows radiologists to prioritize those cases and focus greater attention where it is needed.
Transpara has been proven to decrease radiologists’ workload, reduce the time needed to read exams, and improve accuracy. Ultimately, this delivers better breast care for radiologists and women alike:
- The use of Transpara reduced false positives and radiologists’ workload by up to 62.6% in largest Danish screening program, “An Artificial Intelligence–based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload,” Radiology
- Across a study cohort of over 100,000 women, Transpara-assisted workflow helped detect 29% more cancers while safely reducing reading workload by 44% with no significant change in recall rate. This research also represented the first time that a breast AI solution is featured in a randomized controlled trial, the Mammography Screening with AI (MASAI) Study. The Lancet Digital Health
Better Workflow can Lead to Better Outcomes
Advancements in breast imaging technology have enabled higher precision in diagnosing disease. However, they also come with new challenges associated with the intricate balance of workload, accuracy, and efficiency. Such challenges raise questions about sustainability within the field and call for systemic solutions that prioritize both patient outcomes and radiologist well-being.
To address these challenges, AI tools for breast imaging can support radiologists by providing decision support to enhance clinical accuracy, lessen workload, and optimize workflow. The goal of practices adopting Breast AI is to improve early detection of cancer and provide accurate and consistent breast cancer screening to all women.
[1] Waite S, Kolla S, Jeudy J, et al. Tired in the reading room: the influence of fatigue in radiology. J Am Coll Radiol 2017; 14:191–197
[2] Jay R Parikh, Jia Sun, Martha B Mainiero, Prevalence of Burnout in Breast Imaging Radiologists, Journal of Breast Imaging, Volume 2, Issue 2, March/April 2020, Pages 112–118, https://doi.org/10.1093/jbi/wbz091
[3] Shanafelt, Tait D., et al. “Changes in burnout and satisfaction with work-life integration in physicians and the general US working population between 2011 and 2017.” Mayo Clinic Proceedings. Vol. 94. No. 9. Elsevier, 2019.
