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ARXIV:2603.11413 · CONSUMER HEALTH AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.11413CONSUMER HEALTH AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
This paper critiques the evaluation methods of consumer health AI triage systems, emphasizing the need for realistic testing conditions.
Opportunity summary
Pain This paper critiques the evaluation methods of consumer health AI triage systems, emphasizing the need for realistic testing conditions.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
This paper critiques the evaluation methods of consumer health AI triage systems, emphasizing the need for realistic testing conditions. reported in \textit{Nature Medicine} that ChatGPT Health under-triages 51.6\% of emergencies, concluding that consumer-facing AI…
Ramaswamy et al. reported in \textit{Nature Medicine} that ChatGPT Health under-triages 51.6\% of emergencies, concluding that consumer-facing AI triage poses safety risks.
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Prompt-faithful checks on the authors' exact released prompts confirmed the scaffold produces model-dependent, case-dependent results.
Consumer Health AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
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This paper critiques the evaluation methods of consumer health AI triage systems, emphasizing the need for realistic testing conditions.
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10.48550/arXiv.2603.11413This paper critiques the evaluation methods of consumer health AI triage systems, emphasizing the need for realistic testing conditions.
Abstract
Ramaswamy et al. reported in \textit{Nature Medicine} that ChatGPT Health under-triages 51.6\% of emergencies, concluding that consumer-facing AI triage poses safety risks. However, their evaluation used an exam-style protocol -- forced A/B/C/D output, knowledge suppression, and suppression of clarifying questions -- that differs fundamentally from how consumers use health chatbots. We tested five frontier LLMs (GPT-5.2, Claude Sonnet 4.6, Claude Opus 4.6, Gemini 3 Flash, Gemini 3.1 Pro) on a 17-scenario partial replication bank under constrained (exam-style, 1,275 trials) and naturalistic (patient-style messages, 850 trials) conditions, with targeted ablations and prompt-faithful checks using the authors' released prompts. Naturalistic interaction improved triage accuracy by 6.4 percentage points ($p = 0.015$). Diabetic ketoacidosis was correctly triaged in 100\% of trials across all models and conditions. Asthma triage improved from 48\% to 80\%. The forced A/B/C/D format was the dominant failure mechanism: three models scored 0--24\% with forced choice but 100\% with free text (all $p < 10^{-8}$), consistently recommending emergency care in their own words while the forced-choice format registered under-triage. Prompt-faithful checks on the authors' exact released prompts confirmed the scaffold produces model-dependent, case-dependent results. The headline under-triage rate is highly contingent on evaluation format and should not be interpreted as a stable estimate of deployed triage behavior. Valid evaluation of consumer health AI requires testing under conditions that reflect actual use.
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unverified0 refs; 0 sources; 17% coverage.
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Dimensions overall score 3.0
PROBLEM
This paper critiques the evaluation methods of consumer health AI triage systems, emphasizing the need for realistic testing conditions. reported in \textit{Nature Medicine} that ChatGPT Health under-triages 51.6\% of emergencies, concluding that consumer-facing AI triage poses...
METHOD
Ramaswamy et al. reported in \textit{Nature Medicine} that ChatGPT Health under-triages 51.6\% of emergencies, concluding that consumer-facing AI triage poses safety risks.
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Prompt-faithful checks on the authors' exact released prompts confirmed the scaffold produces model-dependent, case-dependent results.
WHY NOW
Consumer Health AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
This paper critiques the evaluation methods of consumer health AI triage systems, emphasizing the need for realistic testing conditions. reported in \textit{Nature Medicine} that ChatGPT Health under-triages 51.6\% of emergencies, concluding that consumer-facing AI triage poses safety risks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Ramaswamy et al. reported in \textit{Nature Medicine} that ChatGPT Health under-triages 51.6\% of emergencies, concluding that consumer-facing AI triage poses safety risks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Prompt-faithful checks on the authors' exact released prompts confirmed the scaffold produces model-dependent, case-dependent results.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Consumer Health AI moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This paper critiques the evaluation methods of consumer health AI triage systems, emphasizing the need for realistic testing conditions.
Segment
Consumer Health AI
Adoption evidence
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Commercial read
3.0/10 public viability
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reason
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unverified
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Technical feasibility
partial
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0 references, 0 sources, 17% evidence coverage.
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