aceLarge Language Model Chatbot Responses to Cancer Survivorship Questions in Hong Kong: Bilingual Evaluation and Prompt Optimization Study
Background As the population of survivors of cancer grows and new information technologies become widespread in Hong Kong, survivors of cancer increasingly seek ongoing care and support information from large language models (LLMs). While these tools provide immediate conversational responses, they carry substantial risks of generating inaccurate, unsafe, or generic medical advice. Evaluating LLM response performance is particularly critical in Hong Kong's bilingual health care context. Objective This study aime…
In an exploratory within-sample comparison, an expert-designed CRAFT prompt raised Gemini 3.1 Pro's clinical utility for cancer survivorship questions and corrected baseline unsafe outputs without reducing understandability.
Unconstrained chatbots gave unsafe cancer survivorship answers in both languages, with up to 24% English errors and up to 20% Traditional Chinese errors.
The prompt comparison was an exploratory within-sample analysis, and the link between readability and clinical utility was not formally tested, so the results should not be treated as confirmed general performance.
Evidence
- Peer-reviewedJournal of Medical Internet Research2026-10-08
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Truvace Impact Record TRV-2026-1702, v1: “Large Language Model Chatbot Responses to Cancer Survivorship Questions in Hong Kong: Bilingual Evaluation and Prompt Optimization Study.” Truvace, 2026-10-17. /record/TRV-2026-1702 (accessed at citation time). sha256 4adf35795b3aa69e…
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