ArticleLa Radiologia medica2025
Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography.
Article in La Radiologia medica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Beyond detection: can vision-language models redefine chest radiograph interpretation?European radiology · 2026Article
- Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.Journal of imaging informatics in medicine · 2026Review
- A five-phase evaluation framework for diagnostic and predictive medical artificial intelligence.NPJ digital medicine · 2026Article
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Determinants of Trust and Reliance on Artificial Intelligence in Clinical Settings: A Study on Physicians' Use of Large Language Model in Psychiatric Symptom Assessment.Psychiatry investigation · 2026Article
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Patient and physician perspectives on large language model generated responses about brain aneurysm.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2026Article
- Doctors vs. Algorithms: Physicians, too, struggle to learn from evidence that contradicts AI suggestions.PLOS digital health · 2026Article
- Review
- Evaluating large language model-generated brain MRI protocols: performance of GPT4o, o3-mini, DeepSeek-R1 and Qwen2.5-72B.European radiology · 2026Article
- Evaluation of Approved AI-based Brain Aneurysm Detection Software in Clinical Practice: Comparison with Radiologist Assessment and Image Re-review.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- From model failure to system harm: operationalizing a sociotechnical pathway for healthcare AI safety.Frontiers in artificial intelligence · 2026Article
- Exposing the fragility of LLM reasoning through bias-inducing prompts: evidence from BiasMedQA.BMJ digital health & AI · 2026Article
- Boosting LLM-assisted diagnosis: 10-minute LLM tutorial elevates radiology residents' performance in brain MRI interpretation.Neuroradiology · 2025Article
- Radiological Reporting of Brain Atrophy in MRI: Real-Life Comparison Between Narrative Reports, Semiquantitative Scales and Automated Software-Based Volumetry.Diagnostics (Basel, Switzerland) · 2025Article
- Benchmarking the diagnostic performance of open source LLMs in 1933 Eurorad case reports.NPJ digital medicine · 2025Article
- Research progress in medical imaging for intracranial aneurysms: technological advances in diagnosis, management, and clinical integration.Frontiers in neurology · 2025Review
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo 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
methodsNine 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.
resultsFalse-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.
conclusionOur 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.
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