Objectives We report our experience implementing an algorithm for the detection of large vessel occlusion (LVO) for suspected stroke in the emergency setting, including its performance, and offer an explanation as to why it was poorly received by radiologists. Materials and methods An algorithm was deployed in the emergency room at a single tertiary care hospital for the detection of LVO on CT angiography (CTA) between September 1st–27th, 2021. A retrospective analysis of the algorithm’s accuracy was performed. Results During the study period, 48 patients underwent CTA examination in the emergency department to evaluate for emergent LVO, with 2 positive cases (60.3 years ± 18.2; 32 women). The LVO algorithm demonstrated a sensitivity and specificity of 100% and 92%, respectively. While the sensitivity of the algorithm at our institution was even higher than the manufacturer’s reported values, the false discovery rate was 67%, leading to the perception that the algorithm was inaccurate. In addition, the positive predictive value at our institution was 33% compared with the manufacturer’s reported values of 95–98%. This disparity can be attributed to differences in disease prevalence of 4.1% at our institution compared with 45.0–62.2% from the manufacturer’s reported values. Conclusion Despite the LVO algorithm’s accuracy performing as advertised, it was perceived as inaccurate due to more false positives than anticipated and was removed from clinical practice. This was likely due to a cognitive bias called the accuracy paradox. To mitigate the accuracy paradox, radiologists should be presented with metrics based on a disease prevalence similar to their practice when evaluating and utilizing artificial intelligence tools. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> An artificial intelligence algorithm for detecting emergent LVOs was implemented in an emergency department, but it was perceived to be inaccurate. <Emphasis Type="BoldItalic">Findings</Emphasis> Although the algorithm’s accuracy was both high and as advertised, the algorithm demonstrated a high false discovery rate. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> The misperception of the algorithm’s inaccuracy was likely due to a special case of the base rate fallacy—the accuracy paradox. Equipping radiologists with an algorithm’s false discovery rate based on local prevalence will ensure realistic expectations for real-world performance.

Implementing an AI algorithm in the clinical setting: a case study for the accuracy paradox

期刊论文
发表日期：

2024年12月31日

查看全文

John A. Scaringi,
Ryan A. McTaggart,
Matthew D. Alvin,
Michael Atalay,
Michael H. Bernstein,
Mahesh V. Jayaraman,
Gaurav Jindal,
Jonathan S. Movson,
David W. Swenson,
Grayson L. Baird

摘要

Objectives

We report our experience implementing an algorithm for the detection of large vessel occlusion (LVO) for suspected stroke in the emergency setting, including its performance, and offer an explanation as to why it was poorly received by radiologists.

Materials and methods

An algorithm was deployed in the emergency room at a single tertiary care hospital for the detection of LVO on CT angiography (CTA) between September 1st–27th, 2021. A retrospective analysis of the algorithm’s accuracy was performed.

Results

During the study period, 48 patients underwent CTA examination in the emergency department to evaluate for emergent LVO, with 2 positive cases (60.3 years ± 18.2; 32 women). The LVO algorithm demonstrated a sensitivity and specificity of 100% and 92%, respectively. While the sensitivity of the algorithm at our institution was even higher than the manufacturer’s reported values, the false discovery rate was 67%, leading to the perception that the algorithm was inaccurate. In addition, the positive predictive value at our institution was 33% compared with the manufacturer’s reported values of 95–98%. This disparity can be attributed to differences in disease prevalence of 4.1% at our institution compared with 45.0–62.2% from the manufacturer’s reported values.

Conclusion

Despite the LVO algorithm’s accuracy performing as advertised, it was perceived as inaccurate due to more false positives than anticipated and was removed from clinical practice. This was likely due to a cognitive bias called the accuracy paradox. To mitigate the accuracy paradox, radiologists should be presented with metrics based on a disease prevalence similar to their practice when evaluating and utilizing artificial intelligence tools.

Key Points

Question An artificial intelligence algorithm for detecting emergent LVOs was implemented in an emergency department, but it was perceived to be inaccurate.

Findings Although the algorithm’s accuracy was both high and as advertised, the algorithm demonstrated a high false discovery rate.

Clinical relevance The misperception of the algorithm’s inaccuracy was likely due to a special case of the base rate fallacy—the accuracy paradox. Equipping radiologists with an algorithm’s false discovery rate based on local prevalence will ensure realistic expectations for real-world performance.