Emily contra Lakisha: Cuantificación de las disparidades socioeconómicas y estructurales en los planes de atención...

Rapid integration of Large Language Models (LLMs) into oncology workflows mandates scrutiny of implicit biases that may exacerbate health inequities. This study quantifies structural biases in LLM-generated referral plans for Advanced Ovarian Cancer.

We conducted a cross-sectional, randomized algorithmic audit (N = 320) of four state-of-the-art LLMs (ChatGPT 5.2, Gemini 3, Grok 4.1, Claude 4.5) using a 2 × 2 factorial vignette design to disentangle the effects of onomastic Race proxy (Emily vs. Lakisha) from Geography (Boston vs. Indianola). Responses were evaluated using the validated "Automated Structural Competence Scoring (ASCS) Pipeline" across 23 metrics assessing clinical concordance, prioritization, and linguistic tone.

Models exhibited a critical "Medical-Structural Dissociation." While maintaining high clinical guideline adherence regardless of demographics, algorithms displayed severe structural incompetence for the intersectional phenotype (Black/Rural). Sensitivity analysis revealed that 'Financial Fixation' was driven almost exclusively by Race (p < 0.001), with models being 16.8 times more likely (OR: 16.80) to introduce unprompted cost warnings for 'Lakisha' compared to 'Emily,' regardless of location.

Current LLMs are clinically accurate but structurally biased. By hallucinating financial scarcity based on onomastic signals and failing to prioritize logistical solutions for rural patients, these models risk imposing a cognitive burden on physicians and inducing "algorithmic redlining." Safe deployment in primary care requires moving beyond biomedical fidelity toward "Structural Alignment," ensuring AI functions as a cognitive prosthetic that mitigates, rather than entrenches, systemic disparities.