对比AI与人类撰写新冠谣言辟谣内容，发现人更爱读AI的，但AI不够有说服力。

With the wide adoption of large language models (LLMs) in information assistance, it is essential to examine their alignment with human communication styles and values. We situate this study within health fact-checking, where effective communication is critical for correcting misconceptions and building trust. Although recent studies have explored LLMs for fact-checking and health communication, differences between LLM and human communication styles and associated reader perceptions remain under-explored. We compiled a dataset of 1,498 health misinformation claims and explanations from authoritative fact-checking organizations and generated LLM responses to inaccurate health information. Drawing on health communication theories, we evaluated communication styles across three dimensions: information linguistic features, sender persuasive strategies, and receiver value alignments. We further assessed reader perceptions through a blinded evaluation with 99 participants. LLM-generated articles scored significantly lower in persuasive strategies, certainty expressions, and alignment with social values and moral foundations. However, participants strongly preferred LLM content, with over 60% of responses favoring LLM articles for clarity, completeness, and persuasiveness. This preference was associated with structured presentation, clarity, and neutral tone, which may convey completeness and professionalism despite reduced nuance or rigor. These findings highlight both the potential and limitations of LLMs in health communication and fact-checking, suggesting that reader preference may not necessarily correspond to established measures of communication quality.