Automation Bias in AI-Assisted Detection of Cerebral Aneurysms on Time-of-Flight MR-Angiography Article Swipe
Su Hwan Kim
,
Severin Schramm
,
Evamaria Olga Riedel
,
Lena Schmitzer
,
Enrike Rosenkranz
,
Olivia Kertels
,
Jannis Bodden
,
Karolin J. Paprottka
,
Dominik Sepp
,
Martin Renz
,
Jan S. Kirschke
,
Thomas Baum
,
Christian Maegerlein
,
Tobias Boeckh‐Behrens
,
Claus Zimmer
,
Benedikt Wiestler
,
Dennis M. Hedderich
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.05.31.24308021
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.05.31.24308021
Background AI systems have the potential to support in detecting cerebral aneurysms. Yet, the role of automation bias (inclination of humans to overly rely on automated decision-making systems) in AI-assisted cerebral aneurysm detection remains unclear. Purpose To determine how automation bias can affect radiologists with varying experience levels when reading time-of-flight magnetic resonance angiography (TOF-MRA) studies with the assistance of an AI system for cerebral aneurysm detection. Methods In this prospective experiment, nine radiologists with varying levels of experience evaluated twenty TOF-MRA exams for the presence of anterior circulation aneurysms, with each arterial segment rated on a 4-point Likert scale, and provided follow-up recommendations. Every case was evaluated twice (with or without assistance by the AI software © mdbrain), with a washout-period of at least four weeks between the two sessions. Ten out of twenty 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. A thematic analysis was performed to summarize reader feedback and observations. Results False-positive AI results led to significantly higher suspicion of aneurysm findings (p = 0.01). Inexperienced readers further recommended significantly more aggressive 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 seconds; p < 0.001), moderately experienced (126.2 vs 156.5 seconds; p < 0.009), and very experienced (117.9 vs 153.5 seconds; p < 0.001) readers alike. Conclusion Our results demonstrate susceptibility of radiology readers to automation bias in detecting cerebral aneurysms in TOF-MRA studies when encountering false-positive AI findings. In inexperienced readers, this behavior further translated into more aggressive follow-up recommendations. AI assistance resulted in significantly shorter reading times across experience levels. 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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.05.31.24308021
- https://www.medrxiv.org/content/medrxiv/early/2024/06/03/2024.05.31.24308021.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399279497
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399279497Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.05.31.24308021Digital Object Identifier
- Title
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Automation Bias in AI-Assisted Detection of Cerebral Aneurysms on Time-of-Flight MR-AngiographyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-03Full publication date if available
- Authors
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Su Hwan Kim, Severin Schramm, Evamaria Olga Riedel, Lena Schmitzer, Enrike Rosenkranz, Olivia Kertels, Jannis Bodden, Karolin J. Paprottka, Dominik Sepp, Martin Renz, Jan S. Kirschke, Thomas Baum, Christian Maegerlein, Tobias Boeckh‐Behrens, Claus Zimmer, Benedikt Wiestler, Dennis M. HedderichList of authors in order
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https://doi.org/10.1101/2024.05.31.24308021Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2024/06/03/2024.05.31.24308021.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2024/06/03/2024.05.31.24308021.full.pdfDirect OA link when available
- Concepts
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Automation, Angiography, Cerebral angiography, Aneurysm, Radiology, Medicine, Computer science, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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26Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.While | 286 |
| abstract_inverted_index.cases | 136 |
| abstract_inverted_index.exams | 83 |
| abstract_inverted_index.least | 125, 139 |
| abstract_inverted_index.rated | 95 |
| abstract_inverted_index.test. | 157 |
| abstract_inverted_index.times | 150, 203, 282 |
| abstract_inverted_index.twice | 109 |
| abstract_inverted_index.using | 153 |
| abstract_inverted_index.weeks | 127 |
| abstract_inverted_index.(117.9 | 231 |
| abstract_inverted_index.(126.2 | 221 |
| abstract_inverted_index.(164.1 | 212 |
| abstract_inverted_index.0.001) | 237 |
| abstract_inverted_index.0.01). | 183 |
| abstract_inverted_index.Likert | 99 |
| abstract_inverted_index.across | 283 |
| abstract_inverted_index.affect | 43 |
| abstract_inverted_index.alike. | 239 |
| abstract_inverted_index.ensure | 305 |
| abstract_inverted_index.higher | 176 |
| abstract_inverted_index.humans | 21 |
| abstract_inverted_index.levels | 48, 77 |
| abstract_inverted_index.overly | 23 |
| abstract_inverted_index.reader | 165 |
| abstract_inverted_index.scale, | 100 |
| abstract_inverted_index.system | 63 |
| abstract_inverted_index.twenty | 81, 135 |
| abstract_inverted_index.0.001), | 218 |
| abstract_inverted_index.0.005). | 201 |
| abstract_inverted_index.0.009), | 227 |
| abstract_inverted_index.4-point | 98 |
| abstract_inverted_index.Methods | 68 |
| abstract_inverted_index.Purpose | 36 |
| abstract_inverted_index.Reading | 202 |
| abstract_inverted_index.Results | 169 |
| abstract_inverted_index.TOF-MRA | 82, 256 |
| abstract_inverted_index.between | 128 |
| abstract_inverted_index.further | 186, 268 |
| abstract_inverted_index.levels. | 285 |
| abstract_inverted_index.provide | 294 |
| abstract_inverted_index.readers | 185, 238, 247 |
| abstract_inverted_index.reading | 50, 149, 281 |
| abstract_inverted_index.remains | 34 |
| abstract_inverted_index.results | 172, 242 |
| abstract_inverted_index.segment | 94 |
| abstract_inverted_index.shorter | 206, 280 |
| abstract_inverted_index.studies | 56, 257 |
| abstract_inverted_index.support | 8 |
| abstract_inverted_index.systems | 3, 288 |
| abstract_inverted_index.varying | 46, 76 |
| abstract_inverted_index.without | 112 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Aneurysm | 144 |
| abstract_inverted_index.Wilcoxon | 155 |
| abstract_inverted_index.analysis | 160 |
| abstract_inverted_index.aneurysm | 32, 66, 179, 291 |
| abstract_inverted_index.anterior | 88 |
| abstract_inverted_index.arterial | 93 |
| abstract_inverted_index.assessed | 152 |
| abstract_inverted_index.behavior | 267 |
| abstract_inverted_index.cerebral | 11, 31, 65, 253, 290 |
| abstract_inverted_index.feedback | 166 |
| abstract_inverted_index.finding. | 143 |
| abstract_inverted_index.findings | 180, 198 |
| abstract_inverted_index.human-AI | 298 |
| abstract_inverted_index.included | 137 |
| abstract_inverted_index.magnetic | 52 |
| abstract_inverted_index.presence | 86 |
| abstract_inverted_index.provided | 102 |
| abstract_inverted_index.ratings, | 145 |
| abstract_inverted_index.readers, | 265 |
| abstract_inverted_index.resulted | 277 |
| abstract_inverted_index.seconds; | 215, 224, 234 |
| abstract_inverted_index.software | 117 |
| abstract_inverted_index.systems) | 28 |
| abstract_inverted_index.thematic | 159 |
| abstract_inverted_index.unclear. | 35 |
| abstract_inverted_index.(TOF-MRA) | 55 |
| abstract_inverted_index.adoption. | 309 |
| abstract_inverted_index.aneurysms | 254 |
| abstract_inverted_index.automated | 26 |
| abstract_inverted_index.benefits, | 295 |
| abstract_inverted_index.detecting | 10, 252 |
| abstract_inverted_index.detection | 33, 292 |
| abstract_inverted_index.determine | 38 |
| abstract_inverted_index.effective | 308 |
| abstract_inverted_index.evaluated | 80, 108 |
| abstract_inverted_index.findings. | 262 |
| abstract_inverted_index.follow-up | 103, 146, 191, 273 |
| abstract_inverted_index.mdbrain), | 119 |
| abstract_inverted_index.mitigated | 303 |
| abstract_inverted_index.performed | 162 |
| abstract_inverted_index.potential | 6 |
| abstract_inverted_index.presented | 194 |
| abstract_inverted_index.radiology | 246 |
| abstract_inverted_index.resonance | 53 |
| abstract_inverted_index.sessions. | 131 |
| abstract_inverted_index.summarize | 164 |
| abstract_inverted_index.suspicion | 177 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Conclusion | 240 |
| abstract_inverted_index.aggressive | 190, 272 |
| abstract_inverted_index.aneurysms, | 90 |
| abstract_inverted_index.aneurysms. | 12 |
| abstract_inverted_index.assistance | 59, 113, 209, 276 |
| abstract_inverted_index.automation | 17, 40, 249 |
| abstract_inverted_index.challenges | 296 |
| abstract_inverted_index.detection. | 67 |
| abstract_inverted_index.experience | 47, 79, 284 |
| abstract_inverted_index.moderately | 219 |
| abstract_inverted_index.translated | 269 |
| abstract_inverted_index.AI-assisted | 30 |
| abstract_inverted_index.angiography | 54 |
| abstract_inverted_index.circulation | 89 |
| abstract_inverted_index.demonstrate | 243 |
| abstract_inverted_index.experienced | 220, 230 |
| abstract_inverted_index.experiment, | 72 |
| abstract_inverted_index.interaction | 299 |
| abstract_inverted_index.prospective | 71 |
| abstract_inverted_index.recommended | 187 |
| abstract_inverted_index.signed-rank | 156 |
| abstract_inverted_index.(inclination | 19 |
| abstract_inverted_index.encountering | 259 |
| abstract_inverted_index.examinations | 192 |
| abstract_inverted_index.radiologists | 44, 74 |
| abstract_inverted_index.Inexperienced | 184 |
| abstract_inverted_index.inexperienced | 211, 264 |
| abstract_inverted_index.observations. | 168 |
| abstract_inverted_index.significantly | 175, 188, 205, 279 |
| abstract_inverted_index.False-positive | 170 |
| abstract_inverted_index.false-positive | 141, 196, 260 |
| abstract_inverted_index.susceptibility | 244 |
| abstract_inverted_index.time-of-flight | 51 |
| abstract_inverted_index.washout-period | 122 |
| abstract_inverted_index.decision-making | 27 |
| abstract_inverted_index.recommendations, | 147 |
| abstract_inverted_index.recommendations. | 104, 274 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5064415094 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 17 |
| corresponding_institution_ids | https://openalex.org/I2802619606, https://openalex.org/I62916508 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| sustainable_development_goals[1].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[1].score | 0.4399999976158142 |
| sustainable_development_goals[1].display_name | Quality Education |
| citation_normalized_percentile.value | 0.63863733 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |