Understanding and Mitigating Bias in Imaging Artificial Intelligence
Open accessAbstract
Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions). Clinical decisions informed by biased models may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities due to differing performance among patient populations. However, while inequitable bias can harm patients in this context, a mindful approach leveraging equitable bias can address underrepresentation of minority groups or rare diseases. Radiologists should also be aware of bias after AI deployment such as automation bias, or a tendency to agree with automated decisions despite contrary evidence. Understanding common sources of imaging AI bias and the consequences of using biased models can guide preventive measures to mitigate its impact. Accordingly, the authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development. The authors review definitions of bias in AI, describe common sources of bias, and present recommendations to guide quality control measures to mitigate the impact of bias in imaging AI. Understanding the terms featured in this article will enable a proactive approach to identifying and mitigating bias in imaging AI. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Rouzrokh and Erickson in this issue.
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As AI tools learn from archival data, a narrowed data source results in AI models that are not generalizable in heterogeneous patient populations with different demographics, clinical characteristics, and disease prevalence, leading to perpetuated bias in the final AI model.
“As AI tools learn from archival data, a narrowed data source results in AI models that are not generalizable in heterogeneous patient populations with different demographics, clinical characteristics, and disease prevalence, leading to perpetuated bias in the final AI model.120, 121”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [120]
Underrepresentation or overrepresentation of diseases or events compared with their prevalence in the general population or clinical practice causes class imbalance due to an uneven distribution between the training data and the actual population to which the AI model is applied.
“This can occur when some diseases or events are collected with underrepresentation or overrepresentation compared with their prevalence in the general population or clinical practice due to the limited patient diversity included in the training data; this causes a class imbalance due to an uneven distribution between the training data and the actual population to which the AI model is applied.120”
From Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects (2024) · cites this paper as [120]
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Ethical Challenges and Bias in AI Marketing
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- Authors
- Tejani, A. S., Ng, Y. S., Xi, Y., & Rayan, J. C.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Venue
- RadioGraphics
- Published
- 2024-05-01
- Volume
- 44
- Issue
- 5
- Pages
- e230067-e230067
- ISSN
- 0271-5333 (ISSN-L)
- 1527-1323
- OpenAlex
- W4394946404
- Cited by
- 138
- References
- 52
- Citations by year
- 2026: 59 · 2025: 65 · 2024: 13 · 2022: 1
- FWCI
- 12.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Radiographics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic bias, health disparities, cognitive biases, statistical bias, automation bias, minority groups, rare diseases, bias mitigation, quality control, radiology, machine learning lifecycle
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Automation, Humans, Bias
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