Importance: Large language models are increasingly used for clinical decision support yet may perpetuate socioeconomic biases. Whether simple prompt-based interventions can mitigate such biases remains unknown. Objective: To determine whether a prompt-based "inoculation" instructing large-language-models (LLMs) to disregard clinically irrelevant information can reduce bias and improve accuracy in recommendations. Design: Experimental study conducted November 21 to December 11, 2025. Each clinical vignette was presented 10 times per condition to account for stochastic variance. Setting: Publicly available web interfaces of six frontier LLMs with memory features disabled. Participants: No real patients were involved. Two fictional epilepsy vignettes (diagnostic and therapeutic) were created with identical clinical features but differing socioeconomic (SES) descriptors. Main Outcomes and Measures: Accuracy (proportion of responses concordant with guidelines) and bias (accuracy difference between high and low SES vignettes), assessed via binary scoring based on evidence-based guidelines. Results: A total of 480 LLM responses were analyzed. For diagnosis, base accuracy was 36% (43/120), with 45 percentage point bias gap (high SES 58% vs. low SES 13%); inoculation improved accuracy to 55% (66/120) and reduced bias to 27 percentage points. For treatment, base accuracy was 51% (61/120) with 25 percentage point bias gap; inoculation improved accuracy to 63% (75/120) and reduced bias to 8 percentage points. Responses to inoculation varied considerably: Gemini 3 Pro showed complete diagnostic bias elimination (low SES accuracy 0% --> 100%), while Sonnet 4.5 showed paradoxical worsening. Conclusions and Relevance: A simple prompt-based intervention overall reduced socioeconomic bias and improved accuracy in LLM clinical recommendations, though effects varied across models. Prompt engineering may offer a practical approach to mitigating specific AI bias in healthcare.
Bhansali et al. (Fri,) studied this question.