Implicit bias in digital health: systematic biases in large language models’ representation of global public health attitudes and challenges to health equity

Introduction As emerging instruments in digital health, large language models (LLMs) assimilate values and attitudes from human-generated data, thereby possessing the latent capacity to reflect public health perspectives. This study investigates into the representational biases of LLMs through the lens of health equity. We propose and empirically validate a three-dimensional explanatory framework encompassing Data Resources, Opinion Distribution, and Prompt Language, positing that prompts are not just communicative media but critical conduits that embed cultural context. Methods Utilizing a selection of prominent LLMs from the United States and China-namely Gemini 2.5 Pro, GPT-5, DeepSeek-V3, and Qwen 3. We conduct a systematic empirical analysis of their performance in representing health attitudes across diverse nations and demographic strata. Results Our findings demonstrate that: first, the accessibility of data resources is a primary determinant of an LLM’s representational fidelity