Evaluating the quality of consumer health information across the web, large language models, and search engines: a review of criteria, instruments, and methods

Abstract The rapid expansion of internet-based health resources and large language models (LLMs) has substantially increased public access to consumer health information. Although this accessibility offers notable benefits, it also raises concerns about the reliability, accuracy, and trustworthiness of information obtained from conventional web-based platforms and AI-generated content. Despite extensive research, no universally accepted definition or standardised framework exists for evaluating the quality of consumer health information. This narrative review examines how consumer health information quality is defined and evaluated across web-based and LLM-generated environments, and assesses the role of search engine ranking mechanisms in shaping the visibility and accessibility of such information. These domains are considered interconnected components of the consumer health information ecosystem: information is provided or generated through different sources, discovered through search and ranking mechanisms, and evaluated using health information quality criteria. The review synthesizes evaluation criteria, assessment instruments, frameworks, and methodological approaches and compares commonly applied dimensions, including accuracy, readability, coherence, reliability, medical adequacy, and completeness. The findings reveal substantial variation in evaluation criteria and methodologies, along with a lack of consensus on appropriate approaches to assessing the quality of consumer health information. The review also identifies limitations in applying traditional evaluation tools to LLM-generated information and highlights the distinction between information quality and information visibility. These findings support the need for a standardized, multidimensional framework for evaluating consumer health information across web-based and AI-generated environments while accounting for information discovery and ranking mechanisms.