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TRV-2026-0315Certified recordPeer-reviewed

Triage safety of patient-facing AI chatbots for nipple discharge: A guideline-informed assessment of red-flag recognition and patient actionability

Objective To evaluate red-flag recognition, clinical safety, and the quality of patient actionability in responses generated by artificial intelligence (AI) chatbots to patient questions about nipple discharge. Methods This guideline-informed cross-sectional evaluation was conducted to assess the performance of AI chatbots in simulated nipple discharge consultations. A total of 36 English-language simulated patient questions were developed on the basis of clinical guidelines and real-world consultation scenarios…

Health · The Trace — both readings · certified 2026-07-20 · v1 · article view · machine-readable

Current reading — gain

In simulated consultations about nipple discharge, AI chatbots recognized most clinical warning features and were rated safe in most responses.

Current reading — problem

A small proportion of chatbot responses were potentially misleading due to missed red-flag features and poor actionability with insufficient action-oriented recommendations.

What this doesn’t fix

Findings are limited to simulated English-language first-turn consultations with few unsafe events, so model comparisons are uncertain.

Evidence

Reader signal

How should this claim be treated?

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Truvace Impact Record TRV-2026-0315, v1: “Triage safety of patient-facing AI chatbots for nipple discharge: A guideline-informed assessment of red-flag recognition and patient actionability.” Truvace, 2026-07-20. /record/TRV-2026-0315 (accessed at citation time). sha256 e5e02f60cdec7b9a…

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