Many radiology residents do not feel a sense of personal accomplishment and experience increasing burnout rates as they move through their residency program.
The rate of burnout appears to increase among radiology residents as they move through their post-graduate years, according to a study published in the American Journal of Roentgenology.
Researchers from Harvard Medical School in Boston, MA, sought to establish the prevalence of burnout among radiology residents in New England relative to residents in other specialties.
The researchers sent out a 31-item survey to all resident members of the New England Roentgen Ray Society, covering 20 programs and 472 residents. The survey contained nine demographic and program-related questions and the 22-item Maslach Burnout Inventory–Health Services Survey. The researchers calculated the emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA), which were then compared with the results of residents from other specialties.
Ninety-four residents (20%) returned their responses, most of which came from first-year residents (31 replies). Twenty-six second-year residents, 19 third-year residents, and 26 fourth-year residents also replied. Almost half of the residents (41) were 30 to 32 years old.
The results showed that of the 94 responses, 37% reported high EE, 48% reported high DP, and 50% reported low PA scores. The EE, DP, and PA scores and rates were low relative to those reported across specialties. Increasing residency year correlated with high EE and high DP, and no other factor significantly correlated with burnout.
The researchers concluded that the high degree of burnout among radiology residents increased over the post-graduate years, but was present in a smaller percentage relative to residents across other specialties. Radiology residents score relatively poorly in PA and therefore addressing PA may be central to improving burnout symptoms overall.
Study: AI Boosts Ultrasound AUC for Predicting Thyroid Malignancy Risk by 34 Percent Over TI-RADS
February 17th 2025In a study involving assessment of over 1,000 thyroid nodules, researchers found the machine learning model led to substantial increases in sensitivity and specificity for estimating the risk of thyroid malignancy over traditional TI-RADS and guidelines from the American Thyroid Association.
Can CT-Based AI Provide Automated Detection of Colorectal Cancer?
February 14th 2025For the assessment of contrast-enhanced abdominopelvic CT exams, an artificial intelligence model demonstrated equivalent or better sensitivity than radiologist readers, and greater than 90 percent specificity for the diagnosis of colorectal cancer.
Emerging PET/CT Agent Shows Promise in Detecting PCa Recurrence in Patients with Low PSA Levels
February 13th 202518F-DCFPyL facilitated detection of recurrent prostate cancer in 51 percent of patients with PSA levels ranging between 0.2 to 0.5 ng/ml, according to new research presented at the American Society of Clinical Oncology Genitourinary Cancers (ASCO-GU) Symposium.