Abstract:OBJECTIVE
Interpretive accuracy varies among radiologists, especially in mammography. This study examines the relationship between radiologists’ confidence in their assessments and their accuracy in interpreting mammograms.
MATERIALS AND METHODS
In this study, 119 community radiologists interpreted 109 expert-defined screening mammography examinations in test sets and rated their confidence in their assessment for each case. They also provided a global assessment of their ability to interpret mammograms. Pos… Show more
“…Our results are comparable to studies on uncertainty in the assessment of mammography images . A low annual volume of mammography assessments leads to a higher frequency of low confidence score and lower negative predictive values in a group of radiologists.…”
“…Our results are comparable to studies on uncertainty in the assessment of mammography images . A low annual volume of mammography assessments leads to a higher frequency of low confidence score and lower negative predictive values in a group of radiologists.…”
“…As this dataset contains the independent assessments by multiple radiologists of the same mammogram (mean number of independent readings per mammogram = 92), and the true status of each mammogram ( S1 Text ), it allows us to investigate the performance of the above CI-rules. We stress that, while the above dataset has recently been used to investigate the performance of individual radiologists [ 29 – 31 ], up to now, its potential to investigate CI in mammography screening has not yet been harnessed.…”
While collective intelligence (CI) is a powerful approach to increase decision accuracy, few attempts have been made to unlock its potential in medical decision-making. Here we investigated the performance of three well-known collective intelligence rules (“majority”, “quorum”, and “weighted quorum”) when applied to mammography screening. For any particular mammogram, these rules aggregate the independent assessments of multiple radiologists into a single decision (recall the patient for additional workup or not). We found that, compared to single radiologists, any of these CI-rules both increases true positives (i.e., recalls of patients with cancer) and decreases false positives (i.e., recalls of patients without cancer), thereby overcoming one of the fundamental limitations to decision accuracy that individual radiologists face. Importantly, we find that all CI-rules systematically outperform even the best-performing individual radiologist in the respective group. Our findings demonstrate that CI can be employed to improve mammography screening; similarly, CI may have the potential to improve medical decision-making in a much wider range of contexts, including many areas of diagnostic imaging and, more generally, diagnostic decisions that are based on the subjective interpretation of evidence.
“…can nonetheless demonstrate significant selfmonitoring ability ('Will I get THIS question right?'). 9 Self-monitoring has also been shown to correlate with accuracy in radiograph interpretation 10,11 (Table 1).…”
Section: Conceptual Modelmentioning
confidence: 99%
“…Eva and Regehr showed that psychology students who have relatively poor self‐assessment ability for a certain knowledge domain (‘Can I predict the number of questions I will get right on this examination?’) can nonetheless demonstrate significant self‐monitoring ability (‘Will I get THIS question right?’) . Self‐monitoring has also been shown to correlate with accuracy in radiograph interpretation (Table ).…”
This study shows that, in medical students learning radiograph interpretation, the development of self-monitoring skills can be measured and should not be assumed to necessarily vary in the same way as the underlying clinical skill.
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