International journal of medical informatics · 2026
Triage safety of patient-facing AI chatbots for nipple discharge: A guideline-informed assessment of red-flag recognition and patient actionability.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Li Qiushi, Sun Yihan, Ju Xingai, Cui Jianchun
Аннотация
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, covering 6 modules of nipple discharge inquiries. Six commonly used AI chatbots responded to all questions independently, yielding 216 first-turn responses. Two reviewers independently evaluated the responses using a guideline-informed reference standard and structured scoring checklist, with disagreements adjudicated by a third senior reviewer. The primary outcome was the proportion of potentially misleading/unsafe responses, and secondary measures included the red-flag recognition rate, patient actionability score, guideline concordance score, total DISCERN score, and error types.
RESULTS: Among 216 responses, 189 (87.5 %) were rated as safe, 19 (8.8 %) contained minor omissions, and 8 (3.7 %; 95 % confidence interval [CI], 1.4 %-6.9 %) met the primary outcome of potentially misleading/unsafe responses; notably, all were classified as potentially misleading, and no potentially unsafe responses were identified. The overall red-flag recognition rate was 90.6 % (1174 of 1296; 95 % CI, 87.8 %-93.2 %), and the median response-level recognition rate was 100.0 % (interquartile range [IQR], 85.7 %-100.0 %). Potentially misleading responses were most commonly attributable to missed red-flag features (4 of 8, 50.0 %) and poor actionability or insufficient action-oriented recommendations (3 of 8, 37.5 %). In exploratory analyses accounting for the repeated-response structure within the same questions, no clear statistical difference in the proportion of potentially misleading/unsafe responses was observed across models; conversely, between-model differences were observed in the red-flag recognition rate, patient actionability score, and total DISCERN score, although safety comparisons across models should be interpreted cautiously because of the low number of primary outcome events.
CONCLUSIONS: In simulated patient consultations about nipple discharge, potentially misleading/unsafe responses from AI chatbots were uncommon, and most prespecified clinical warning features were recognized overall. Nevertheless, some responses demonstrated incomplete red-flag safety-netting and lacked specific recommendations for subsequent action. AI chatbots may serve as preliminary sources of health information; however, professional clinical evaluation cannot be replaced. Therefore, future patient-facing AI systems should incorporate structured red-flag screening and explicit triage recommendations to improve safety and practical utility in symptom-consultation contexts.
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, covering 6 modules of nipple discharge inquiries. Six commonly used AI chatbots responded to all questions independently, yielding 216 first-turn responses. Two reviewers independently evaluated the responses using a guideline-informed reference standard and structured scoring checklist, with disagreements adjudicated by a third senior reviewer. The primary outcome was the proportion of potentially misleading/unsafe responses, and secondary measures included the red-flag recognition rate, patient actionability score, guideline concordance score, total DISCERN score, and error types.
RESULTS: Among 216 responses, 189 (87.5 %) were rated as safe, 19 (8.8 %) contained minor omissions, and 8 (3.7 %; 95 % confidence interval [CI], 1.4 %-6.9 %) met the primary outcome of potentially misleading/unsafe responses; notably, all were classified as potentially misleading, and no potentially unsafe responses were identified. The overall red-flag recognition rate was 90.6 % (1174 of 1296; 95 % CI, 87.8 %-93.2 %), and the median response-level recognition rate was 100.0 % (interquartile range [IQR], 85.7 %-100.0 %). Potentially misleading responses were most commonly attributable to missed red-flag features (4 of 8, 50.0 %) and poor actionability or insufficient action-oriented recommendations (3 of 8, 37.5 %). In exploratory analyses accounting for the repeated-response structure within the same questions, no clear statistical difference in the proportion of potentially misleading/unsafe responses was observed across models; conversely, between-model differences were observed in the red-flag recognition rate, patient actionability score, and total DISCERN score, although safety comparisons across models should be interpreted cautiously because of the low number of primary outcome events.
CONCLUSIONS: In simulated patient consultations about nipple discharge, potentially misleading/unsafe responses from AI chatbots were uncommon, and most prespecified clinical warning features were recognized overall. Nevertheless, some responses demonstrated incomplete red-flag safety-netting and lacked specific recommendations for subsequent action. AI chatbots may serve as preliminary sources of health information; however, professional clinical evaluation cannot be replaced. Therefore, future patient-facing AI systems should incorporate structured red-flag screening and explicit triage recommendations to improve safety and practical utility in symptom-consultation contexts.