JMIR Publications

ABSTRACT

Background:

Online health information seeking is undergoing a major shift with the advent of artificial intelligence (AI)-powered technologies such as voice assistants and large language models (LLMs). While existing heath information seeking behavior (HISB) models have long explained how people find and evaluate health information, less is known about how users engage with these newer tools, particularly tools that provide “one” answer rather than the resources to investigate a number of different sources.

Objective:

This study aimed to explore how people use and perceive AI- and voice-assisted technologies when searching for health information, and to evaluate whether these tools are reshaping traditional patterns of health information seeking and credibility assessment.

Methods:

We conducted in-depth qualitative research with 27 participants (ages 19–80) using a think-aloud protocol. Participants searched for health information across three platforms—Google, ChatGPT, and Alexa—while verbalizing their thought processes. Prompts included both a standardized hypothetical scenario and a personally relevant health query. Sessions were transcribed and analyzed using reflexive thematic analysis to identify patterns in search behavior, perceptions of trust and utility, and differences across platforms and user demographics.

Results:

Participants integrated AI tools into their broader search routines rather than using them in isolation. ChatGPT was valued for its clarity, speed, and ability to generate keywords or summarize complex topics, even by users skeptical of its accuracy. Trust and utility did not always align; participants often used ChatGPT despite concerns about sourcing and bias. Google’s AI overviews were met with caution—participants frequently skipped them to review traditional search results. Alexa was viewed as convenient but limited, particularly for in-depth health queries. Platform choice was influenced by the seriousness of the health issue, context of use, and prior experience. One-third of participants were multilingual and they identified challenges with voice recognition, cultural relevance, and data provenance. Overall, users exhibited sophisticated “mix-and-match” behaviors, drawing on multiple tools depending on context, urgency, and familiarity.

Conclusions:

Classic HISB models remain applicable in the AI era, but search behavior is becoming more dynamic and context-driven. Users selectively engage with AI and voice tools based on perceived usefulness, not just trustworthiness, challenging assumptions that credibility is the primary driver of technology adoption. Findings highlight the need for digital health literacy efforts that help users evaluate both the capabilities and limitations of emerging tools. Given the rapid evolution of search technologies, longitudinal studies, and real-time observation methods are essential for understanding how AI continues to reshape health information seeking.