Whose doctor does the AI recommend? An algorithm audit of reputation and name-based demographic signals in large language model-assisted physician choice.

BACKGROUND

Patients increasingly ask large language model (LLM) assistants which doctor to see. Existing audits observe real, correlated physician profiles, so they cannot isolate what drives these recommendations.

OBJECTIVE

To estimate how reputation signals, fees, list position, and names signaling gender and ethnicity affect LLM physician recommendations in a controlled task.

METHODS

Prespecified randomized choice-based conjoint audit. Seven LLMs (six small open-weight models and GPT-4o-mini) each chose one of five synthetic family-medicine physician cards with independently randomized attributes (3024 choice sets; three personas; nine prompts; eight secondary arms). Gender and ethnicity were signaled only by names. Average marginal component effects were estimated with choice-set-clustered linear probability models, per model and pooled.

RESULTS

Across 40,068 responses, a 3.9→4.7 rating raised choice probability by 31.4 percentage points (pp; 95% CI 30.5-32.3) and a $90→$190 fee lowered it by 20.0 pp (19.1-21.0). Pooled, female-signaled names were chosen 2.5 pp (1.8-3.2) more often than male-signaled names, and Hispanic- and South-Asian-signaled names were chosen 2.8-2.9 pp more often than White-signaled names; these held with names as the units of inference, whereas a Black-signaled estimate (1.3 pp) did not. Specific names within categories differed (placebo P<.001), and name-cue effects varied significantly between models (female-signaled: -0.5 to +7.4 pp; gpt-4o-mini +0.8, CI -0.3 to 1.8). The last-listed card lost 6.2 pp. Gender or ethnicity appeared in ≤0.03% of stated reasons.

CONCLUSIONS

In this panel, recommendations ran mainly on ratings and fees but also responded, in model-specific ways, to name cues and list position that the models' explanations did not mention. These controlled-task differences are not estimates of real-world access; they call for behavioral audits of each model version. The materials will be released for repeat audits.