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Cited by 141Artificial Intelligence in Healthcare and EducationRadiology practices and educationAI in cancer detection

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

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Bernstein, Michael H., Atalay, Michael K., Dibble, Elizabeth H., Maxwell, Aaron W. P., Karam, Adib R., Agarwal, Saurabh, Ward, Robert C., Healey, Terrance T. +1 more·2023·European Radiology
DOI 10.1007/s00330-023-09747-1PMC PMC10235827
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Abstract

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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  • References to prior studies can be removed to reduce hallucinations when prior studies are not available.

    “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”

    From MAIRA-2: Grounded Radiology Report Generation (2024) · cites this paper as [18]

    The Introduction (Current study; page unavailable) and Materials and methods (Manipulations; page unavailable) concern incorrect AI results and whether those results are saved in a patient file. Searches for prior-study references, unavailable prior images, and hallucinations found no passage addressing removal of such references to reduce hallucinations. (LLM, conf=0.76)

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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”

    From MAIRA-2: Grounded Radiology Report Generation (2024) · cites this paper as [45]

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Research works printed but not linked: 2

  1. [20]Hundall C (2021) Thinking about AI? Available via https://www.acr.org/Practice-Management-Quality-Informatics/ACR-Bulletin/Articles/June-2021/Thinking-About-AI. Accessed 30 Mar 2022
  2. [21]Walton D (2010) Appeal to expert opinion: arguments from authority. Penn State Press

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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
Publisher
Springer Science and Business Media LLC
Published
2023-06-02
Volume
33
Issue
11
Pages
8263-8269
ISSN
  • 0938-7994 (ISSN-L)
  • 1432-1084
DOI
10.1007/s00330-023-09747-1
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
Licence
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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