Malpractice suits in chest radiology: An evaluation of the histories of 8265 radiologists
Abstract
Purpose The aim of this study was to present rates of claims, causes of error, percentage of cases resulting in a judgment, and average payments made by radiologists in chest-related malpractice cases in a survey of 8265 radiologists. Materials and methods The malpractice histories of 8265 radiologists were evaluated from the credentialing files of One-Call Medical Inc., a preferred provider organization for computed tomography/magnetic resonance imaging in workers' compensation cases. Results Of the 8265 radiologists, 2680 (32.4%) had at least 1 malpractice suit. Of those who were sued, the rate of claims was 55.1 per 1000 person years. The rate of thorax-related suits was 6.6 claims per 1000 radiology practice years (95% confidence interval, 6.0-7.2). There were 496 suits encompassing 48 different causes. Errors in diagnosis comprised 78.0% of the causes. Failure to diagnose lung cancer was by far the most frequent diagnostic error, representing 211 cases or 42.5%. Of the 496 cases, an outcome was known in 417. Sixty-one percent of these were settled in favor of the plaintiff, with a mean payment of $277,230 (95% confidence interval, 226,967-338,614). Conclusions Errors in diagnosis, and among them failure to diagnose lung cancer, were by far the most common reasons for initiating a malpractice suit against radiologists related to the thorax and its contents.
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Manual review may lead to missed or incorrect diagnoses, complicating efforts to identify lung cancer early.
“Manual review may therefore lead to missed or incorrect diagnoses, complicating efforts to identify lung cancer early [40].”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [40]
Staging accuracy can be influenced by the radiologist's experience, and the complex appearance of lung nodules on CT increases the risk of interpretive errors.
“However, staging accuracy can be influenced by the radiologist’s experience, and the complex appearance of lung nodules on CT increases the risk of interpretive errors.”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [40]
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Chiu, H.-Y., Chao, H.-S., & Chen, Y.-M. (2022) · Cancers · doi:10.3390/cancers14061370 · Cited by 138 · see citing works
Petrella, F., & Rizzo, S. (2026) · Cancers · doi:10.3390/cancers18020246 · Cited by 10 · see citing works
- Authors
- Baker, S. R., Patel, R. H., Yang, L., Lelkes, V. M., & Castro, A.
- Venue
- Journal of Thoracic Imaging
- Published
- 2013-11
- Volume
- 28
- Issue
- 6
- Pages
- 388-391
- ISSN
- 0883-5993
- Cited by
- 48
- References
- 0
- Language
- en
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