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

This paper not only quantifies the degree to which these leading LLMs reflect public health attitudes but also furnishes a robust analytical pathway for dissecting the underlying mechanisms of their representational biases. These findings bear profound implications for the advancement of health equity in the artificial intelligence era.