How U.S. Bias in AI Neuroradiology Poses Global Risks

Recent studies highlight a significant neuroradiology guideline bias in medical artificial intelligence systems. Large language models (LLMs) increasingly assist clinicians with diagnostic decisions and report drafting worldwide. However, their training data often contains geographic imbalances. Consequently, these models may favor certain regional guidelines over others, posing clinical risks in international settings.

The Prevalence of Neuroradiology Guideline Bias

Specifically, a recent study evaluated three state-of-the-art LLMs—GPT-o3, Mistral Large, and DeepSeek R1—alongside MedGemma 1.5. Researchers presented thirty clinical vignettes with conflicting international clinical guidelines to these models. In the implicit setting where the prompt did not specify any target guidelines, all models favored U.S. standards. Furthermore, they aligned with U.S. guidelines in 27 of 30 scenarios, representing a staggering 90% bias rate. This trend persisted even when researchers presented the vignettes in French. Therefore, language translation does not eliminate this embedded preference. Additionally, the bias remained strong even in models developed outside the United States, such as DeepSeek from China and Mistral from France. Thus, model origin does not guarantee geographical neutrality in clinical decision support.

Why Adherence Fails in Explicit Settings

Clinicians might assume that instructing an AI to follow a non-U.S. guideline would solve this problem. However, the study demonstrated that explicit prompts failed to correct the bias. When researchers explicitly instructed the models to follow non-U.S. recommendations, adherence declined sharply. Consequently, simple prompting strategies are insufficient for local contextualization. This failure raises serious clinical and legal concerns for global deployment. For instance, radiologists in India or Europe could receive inappropriate recommendations if they rely blindly on these models. Therefore, healthcare organizations must implement robust safeguards before deploying LLMs for decision support.

Effective Mitigation Strategies for Clinicians

Fortunately, researchers identified a highly effective mitigation strategy during the evaluation. Specifically, providing the complete text of the target guideline restored accuracy above 90% across all models. This in-context learning approach bypasses the model’s pre-trained preferences by forcing it to reference local source documents. Additionally, integrating retrieval-augmented generation (RAG) can automate this process in clinical workflows. By grounding the LLM in local guidelines, healthcare facilities can ensure safe and legally compliant decision support. Ultimately, active clinician oversight and structured localization strategies remain indispensable for the safe integration of artificial intelligence in radiology.

Frequently Asked Questions

Q1: Why do large language models show a bias toward U.S. clinical guidelines?

This bias likely stems from significant geographic imbalances in the training datasets used to build these models. Because U.S. medical literature and guidelines are disproportionately represented in global web crawls and scientific databases, models naturally default to these standards unless they are constrained otherwise.

Q2: Can translating the prompt into other languages resolve the neuroradiology guideline bias?

No, translating the prompt does not eliminate the bias. Studies show that even when vignettes are translated into French or other languages, the underlying AI models continue to favor U.S. guidelines. This indicates that the bias is deeply embedded within the models’ core knowledge structures rather than being a linguistic issue.

Q3: What is the most effective way to ensure an LLM follows local medical guidelines?

The most effective strategy is to provide the complete text of the target guideline directly in the prompt or through a Retrieval-Augmented Generation (RAG) workflow. This forces the model to use the provided clinical text as its primary reference, effectively bypassing its default pre-trained preferences and restoring accuracy above 90%.

References

1. Bazerbachi N et al. Cultural bias in large language models’ ability to follow neuroradiology guidelines. Eur Radiol. 2026 May 26. doi: 10.1007/s00330-026-12634-0. PMID: 42189215.
2. Dietrich G et al. When Framing Shapes the Answer: Cognitive Bias and Large Language Model Reliability in Radiology. Radiology. 2026 May 6. doi: 10.1148/radiol.250482.
3. Poulain R et al. Bias Patterns in the Application of LLMs for Clinical Decision Support. Del Med J. 2026 May 1. doi: 10.32481/djph.2026.03.10.