Social Status and Clinical Resource Allocation by a Large Language Model: An Evaluation of 30,618 Decisions

Objective: The objective was to quantify whether demographic and social attributes that were irrelevant to stated clinical need, prognosis, and expected benefit altered resource-allocation decisions made by a general-purpose large language model (LLM). Methods: We conducted a cross-sectional audit of the gpt-5-chat-latest API model alias on 8 October 2025, across seven clinical vignettes, generating 30,618 forced-choice comparisons between patient profiles. Profiles varied across a full-factorial combination of eight demographic and social attributes while clinical need, prognosis, and expected benefit were held constant. Forced choices were analyzed using pooled logistic regression with separate Patient A and Patient B attribute terms and vignette-specific position effects; position-averaged odds ratios and position-balanced absolute probabilities were derived from this model. Priority-score differences were analyzed using an analogous linear model. Results: The model showed large position-averaged associations between non-clinical patient attributes and allocation decisions. Indigenous and Black race were associated with substantially higher odds of selection relative to White race (Indigenous: OR 16.48, 95% CI 14.85-18.28; Black: OR 8.07, 95% CI 7.32-8.90), corresponding to position-balanced absolute increases in selection probability of 30.7 and 16.3 percentage points, respectively. Conversely, high-status occupation (OR 0.064, 95% CI 0.058-0.071), friendship with institutional leadership (OR 0.121, 95% CI 0.111-0.131), and major donor status (OR 0.092, 95% CI 0.084-0.101) were associated with markedly lower odds of selection. Choice-score concordance was 95.1%. Conclusions: In this controlled audit, the LLM's allocation decisions varied substantially according to demographic and social characteristics despite identical stated clinical need, prognosis, and expected benefit. Although some patterns could be interpreted differently under competing ethical frameworks, their implicit and unexplained incorporation into resource-allocation decisions raises concerns regarding transparency, accountability, and clinical governance. Clinical use of LLM-based allocation support should therefore require explicit safeguards and systematic auditing for non-clinical influences.