In the demanding field of Radiology, fatigue poses a significant challenge, leading to a decline in diagnostic accuracy

In the demanding field of Radiology, medical professionals face a unique set of challenges that contribute to fatigue and adversely impact the accuracy of diagnosis. This blog aims to shed light on how we can leverage technology to mitigate fatigue and increase the accuracy of diagnosis to enhance the wellbeing of radiologists and patient care outcomes.

Krupinski et al.’s ( studies highlight a noticeable decline in diagnostic accuracy associated with fatigue. Their research showed that both general fatigue and visual strain significantly increased after a day of clinical practice, correlating with a decrease in diagnostic accuracy for detecting bone fractures and pulmonary nodules. Specifically, the area under the curve (AUC) for detecting bone fractures dropped from 0.885 (early condition) to 0.852 (late condition), and diagnostic accuracy for identifying pulmonary nodules in CT scans decreased for radiology residents from 79% to 75% after a day of clinical work.

In terms of overnight shifts, Hanna et al.’s ( )study on the effect of overnight shifts found significant increases in fatigue across all domains measured by the SOFI, alongside a notable decrease in diagnostic accuracy for viewing bone radiographs (from 0.926 in non-fatigued conditions to 0.806 in fatigued conditions).

This provides direct evidence of the detrimental effects of acute sleep deprivation on radiological diagnostic performance. In another study by Hanna et al, involving a large dataset of 2,922,377 exams across various medical specialties, we gain critical insights into factors influencing diagnostic discrepancies. Longer shifts correlate significantly with an increased number of major discrepancies in image interpretations, highlighting fatigue as a factor in diagnostic accuracy as the workday progresses. Higher volumes of work are also associated with greater numbers of major discrepancies, suggesting that the pressure and cognitive load of handling many cases adversely affect radiologists’ performance. There is also a notable increase in discrepancies during the latter parts of shifts. 

We think Artificial Intelligence can play a major role in mitigating the above factors. AI algorithms can sift through extensive datasets at a pace unmatchable by human capabilities, significantly reducing the time required to process and analyze radiological images. This speed ensures that diagnoses are delivered faster, enabling quicker treatment decisions, and improving patient outcomes. AI’s prowess in pattern recognition can unearth subtleties and anomalies within images that might elude the human eye. 

This capability enhances the accuracy of diagnoses, especially in detecting diseases at their nascent stages. By learning from vast arrays of imaging data, AI tools are continually refining their diagnostic precision, offering a robust support system to radiologists. A large part of a radiologist’s workload means review of thousands of images that are routine in nature. AI steps in as a crucial ally, automating the analysis of straightforward cases and thus reducing the overall caseload. This intervention allows radiologists to channel their focus and energy towards more complex cases, mitigating fatigue. AI can also pre-screen cases and highlighting areas of concern to ensure that radiologists are presented with a curated selection of challenging cases. This not only hones their diagnostic skills but also enhances the sensitivity of disease detection, thereby elevating the overall quality of diagnostic care.

The positive impacts of AI in the field of radiology are multifaceted, promising not just to enhance the operational aspects of diagnostic imaging but to fundamentally improve the quality of patient care.