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Purpose <p>To determine how automation bias (inclination of humans to overly trust-automated decision-making systems) can affect radiologists when interpreting AI-detected cerebral aneurysm findings in time-of-flight magnetic resonance angiography (TOF-MRA) studies.</p> Material and Methods <p>Nine radiologists with varying levels of experience evaluated twenty TOF-MRA examinations for the presence of cerebral aneurysms. Every case was evaluated with and without assistance by the AI software © mdbrain, with a washout period of at least four weeks in-between. Half of the cases included at least one false-positive AI finding. Aneurysm ratings, follow-up recommendations, and reading times were assessed using the Wilcoxon signed-rank test.</p> Results <p>False-positive AI results led to significantly higher suspicion of aneurysm findings (<i>p</i> = 0.01). Inexperienced readers further recommended significantly more intense follow-up examinations when presented with false-positive AI findings (<i>p</i> = 0.005). Reading times were significantly shorter with AI assistance in inexperienced (164.1 vs 228.2&#xa0;s; <i>p</i> &lt; 0.001), moderately experienced (126.2 vs 156.5&#xa0;s; <i>p</i> &lt; 0.009), and very experienced (117.9 vs 153.5&#xa0;s; <i>p</i> &lt; 0.001) readers alike.</p> Conclusion <p>Our results demonstrate the susceptibility of radiology readers to automation bias in detecting cerebral aneurysms in TOF-MRA studies when encountering false-positive AI findings. While AI systems for cerebral aneurysm detection can provide benefits, challenges in human–AI interaction need to be mitigated to ensure safe and effective adoption.</p>

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Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月12日

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  • Su Hwan Kim,
  • Severin Schramm,
  • Evamaria Olga Riedel,
  • Lena Schmitzer,
  • Enrike Rosenkranz,
  • Olivia Kertels,
  • Jannis Bodden,
  • Karolin Paprottka,
  • Dominik Sepp,
  • Martin Renz,
  • Jan Kirschke,
  • Thomas Baum,
  • Christian Maegerlein,
  • Tobias Boeckh-Behrens,
  • Claus Zimmer,
  • Benedikt Wiestler,
  • Dennis M. Hedderich

摘要

Purpose

To determine how automation bias (inclination of humans to overly trust-automated decision-making systems) can affect radiologists when interpreting AI-detected cerebral aneurysm findings in time-of-flight magnetic resonance angiography (TOF-MRA) studies.

Material and Methods

Nine radiologists with varying levels of experience evaluated twenty TOF-MRA examinations for the presence of cerebral aneurysms. Every case was evaluated with and without assistance by the AI software © mdbrain, with a washout period of at least four weeks in-between. Half of the cases included at least one false-positive AI finding. Aneurysm ratings, follow-up recommendations, and reading times were assessed using the Wilcoxon signed-rank test.

Results

False-positive AI results led to significantly higher suspicion of aneurysm findings (p = 0.01). Inexperienced readers further recommended significantly more intense follow-up examinations when presented with false-positive AI findings (p = 0.005). Reading times were significantly shorter with AI assistance in inexperienced (164.1 vs 228.2 s; p < 0.001), moderately experienced (126.2 vs 156.5 s; p < 0.009), and very experienced (117.9 vs 153.5 s; p < 0.001) readers alike.

Conclusion

Our results demonstrate the susceptibility of radiology readers to automation bias in detecting cerebral aneurysms in TOF-MRA studies when encountering false-positive AI findings. While AI systems for cerebral aneurysm detection can provide benefits, challenges in human–AI interaction need to be mitigated to ensure safe and effective adoption.