arXiv subjects

Qi Han Wong

Publications and source records attributed to Qi Han Wong.

3 recordsLinked to original sources

arXiv2026

Socioeconomic Inference in LLM Medical Triage: Same Symptoms, Different ZIP Code

We investigate whether large language models alter medical triage recommendations for identical symptoms when only the patient's socioeconomic status (SES) varies. Using three deployment-tier models (Gemini 3.5 Flash, Claude Sonnet 4.6, GPT-5.4-mini), we hold a single neurological symptom profile fixed and vary the SES signal along two channels: explicit (insurance status, occupation, housing) and implicit (a US ZIP code, with no other socioeconomic information). All three models raise their emergency-room (ER) referral rate for lower-SES patients given the explicit signal (spreads of 13-50 percentage points). The effect is in the protective direction: lower-SES patients are sent to the ER more often, not less. The model's stated reasoning stays clinically near-identical across conditions, so the shift is invisible to a reasoning-trace audit. Critically, sensitivity to the implicit ZIP-code signal is model-dependent: Gemini infers SES from geography alone, shifting its ER rate by a pooled 11.4 points across six US ZIP-code pairs (p = 1.4e-7, same direction in 6/6 pairs), while Claude Sonnet 4.6 stays flat (-0.1 points) and GPT-5.4-mini shows only a small difference that is not sign-consistent (2.0 points, predicted direction in just 2 of 6 pairs), neither a reliable ZIP-code effect, despite both responding to the explicit signal. This reveals an explicitness gradient in the signal: every model acts on socioeconomic status when it is stated outright, but only Gemini Flash acts on it when it must be inferred from a proxy as thin as five digits. We read this as a model-specific difference rather than a size or cost effect. A single-sentence system-prompt instruction reduces but does not eliminate the effect (Gemini's gap between low- and high-income ZIPs falls from 11.4 to 5.8 points). We release all code, prompts, and raw results.

cs.CY↗

arXiv2026

Gender-Dependent Diagnostic Substitution in LLM Medical Triage: Same Symptoms, Unequal Urgency

We investigate whether large language models produce different medical triage recommendations for identical neurological symptoms when only the patient's stated gender and age vary. Using three model families--Gemini 3.5 Flash, Claude Sonnet 4.6, and GPT-5.4-mini--we present a standardized symptom profile (persistent headache, blurred vision, morning nausea, visual disturbances) across seven demographic conditions: three age groups (25, 38, 65) x two genders (male, female), plus a gender-unspecified baseline (n = 30 per condition per model, 630 total trials). We find a stark, systemic gender-dependent triage disparity: young women receive significantly lower emergency room (ER) referral rates than age-matched men (Gemini: 0% vs. 23.3%; Claude: 6.7% vs. 96.7%; GPT: 6.7% vs. 66.7%, all p < 0.001). The disparity disappears at age 65 for all models. The primary mechanism is diagnostic substitution: the models anchor on a gender-associated diagnosis, preferentially classifying young women with Idiopathic Intracranial Hypertension (IIH)--a condition epidemiologically linked to women of childbearing age--while diagnosing men with generic increased intracranial pressure with space-occupying lesions in the differential. This diagnostic closure routes female patients to lower-urgency care (outpatient doctor appointments) despite comparable severity ratings (7-9/10). Our findings demonstrate that clinical LLMs replicate documented human clinical biases by using epidemiological priors to suppress triage urgency, suggesting that AI triage engines must decouple urgency assessment from probabilistic diagnostic priors. We release all code, prompts, and raw results.

cs.AI↗

arXiv2026

Implicit Geographic Inference in LLM Medical Triage: Language-Driven Disparities in Emergency Recommendations

We investigate whether large language models produce different medical triage recommendations for identical symptoms based solely on the language of the patient prompt. Using Gemini 3.5 Flash, we evaluate a neurological symptom profile (persistent headache, blurred vision, nausea) across six languages (English, Spanish, Chinese, Hindi, Japanese, Arabic) with 30 runs per condition (n=450 total API calls). We find that the model recommends emergency room visits at rates ranging from 0% (Japanese, Hindi) to 30% (English, Arabic), despite assigning nearly identical severity scores (7.7-8.0/10) across all languages. Adding a single sentence specifying the patient's US location increases ER recommendations by up to 76.7 percentage points for non-English prompts, while the reverse anchor (English prompt with a Tokyo location) reduces the ER rate from 30% to 6.7%. A back-translation control (Japanese to English) produces ER rates comparable to the English baseline, confirming that the disparity is not caused by translation quality but by implicit geographic inference from the input language. We release the complete dataset, experiment code, and results.

cs.CL↗