The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Can incorrect artificial intelligence (AI) results impact radiologists, and if so, what can we do about it? A multi-reader pilot study of lung cancer detection with chest radiography
Open accessAbstract
Objective: To examine whether incorrect AI results impact radiologist performance, and if so, whether human factors can be optimized to reduce error. Methods: Multi-reader design, 6 radiologists interpreted 90 identical chest radiographs (follow-up CT needed: yes/no) on four occasions (09/20–01/22). No AI result was provided for session 1. Sham AI results were provided for sessions 2–4, and AI for 12 cases were manipulated to be incorrect (8 false positives (FP), 4 false negatives (FN)) (0.87 ROC-AUC). In the Delete AI (No Box) condition, radiologists were told AI results would not be saved for the evaluation. In Keep AI (No Box) and Keep AI (Box), radiologists were told results would be saved. In Keep AI (Box), the ostensible AI program visually outlined the region of suspicion. AI results were constant between conditions. Results: Relative to the No AI condition (FN = 2.7%, FP = 51.4%), FN and FPs were higher in the Keep AI (No Box) (FN = 33.0%, FP = 86.0%), Delete AI (No Box) (FN = 26.7%, FP = 80.5%), and Keep AI (Box) (FN = to 20.7%, FP = 80.5%) conditions (all p s < 0.05). FNs were higher in the Keep AI (No Box) condition (33.0%) than in the Keep AI (Box) condition (20.7%) (p = 0.04). FPs were higher in the Keep AI (No Box) (86.0%) condition than in the Delete AI (No Box) condition (80.5%) (p = 0.03). Conclusion: Incorrect AI causes radiologists to make incorrect follow-up decisions when they were correct without AI. This effect is mitigated when radiologists believe AI will be deleted from the patient’s file or a box is provided around the region of interest. Clinical relevance statement: When AI is wrong, radiologists make more errors than they would have without AI. Based on human factors psychology, our manuscript provides evidence for two AI implementation strategies that reduce the deleterious effects of incorrect AI. Key Points: • When AI provided incorrect results, false negative and false positive rates among the radiologists increased. • False positives decreased when AI results were deleted, versus kept, in the patient’s record. • False negatives and false positives decreased when AI visually outlined the region of suspicion.
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“Such references can be removed to reduce hallucinations when prior studies are not available, 9,14,18 or used in conjunction with prior images to enable descriptions of change.17,19,20”
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“This bidirectional approach differs from traditional factual verification approaches such as FActScore that assume a 'single' source of truth (e.g., Wikipedia), but has precedents in medical summarisation where both completeness and conciseness are important.45”
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Brady, A. P., Allen, B., Chong, J., Kotter, E., Kottler, N. E., Mongan, J., Oakden‐Rayner, L., dos Santos, D. P., Tang, A., Wald, C., et al. (2024) · Insights into Imaging · doi:10.1186/s13244-023-01541-3 · Cited by 106
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AI in radiology: From promise to practice − A guide to effective integration
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Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review
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Metadata
- Authors
- Bernstein, M. H., Atalay, M. K., Dibble, E. H., Maxwell, A. W. P., Karam, A. R., Agarwal, S., Ward, R. C., Healey, T. T., & Baird, G. L.
- Affiliations
- Brown University (US)
- Corresponding author
- Bernstein, Michael H.
- ORCID
- Venue
- European Radiology
- Published
- 2023-06-02
- Volume
- 33
- Issue
- 11
- Pages
- 8263-8269
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- PMC
- PMC10235827
- OpenAlex
- W4379095447
- Cited by
- 140
- References
- 19
- Citations by year
- 2026: 42 · 2025: 59 · 2024: 35 · 2023: 4
- FWCI
- 5.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- European Radiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- lung cancer detection, chest radiography, radiologist performance, false positives, false-negative results, AI implementation strategy, human factors, trustworthy AI
- MeSH terms
- Radiologists, Artificial Intelligence, Humans, Lung Neoplasms, Pilot Projects, Radiography, Retrospective Studies
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