Authority Signals in AI Cited Health Sources: A Framework for Evaluating Source Credibility in ChatGPT Responses
Erin Jacques (1), Erela Datuowei (2), Vincent Jones II (1), Corey Basch (3), Celeta Vanderpool (2), Nkechi Udeozo (4), Griselda Chapa (1) ((1) York College, CUNY, (2) Teachers College, Columbia University, (3) William Paterson University, (4) CUNY School of Public Health)

TL;DR
This study proposes an Authority Signals Framework to evaluate the credibility of health sources cited by ChatGPT, analyzing 615 sources to understand their institutional backing and digital authority in AI-generated health responses.
Contribution
It introduces a novel framework for assessing source credibility in AI health responses and applies it to analyze cited sources in ChatGPT's outputs.
Findings
Over 75% of sources are from established health institutions.
Sources include reputable organizations like Mayo Clinic and PubMed.
Remaining sources lack formal institutional backing.
Abstract
Health information seeking has fundamentally changed since the onset of Large Language Models (LLM), with nearly one third of ChatGPT's 800 million users asking health questions weekly. Understanding the sources of those AI generated responses is vital, as health organizations and providers are also investing in digital strategies to organically improve their ranking, reach and visibility in LLM systems like ChatGPT. As AI search optimization strategies are gaining maturity, this study introduces an Authority Signals Framework, organized in four domains that reflect key components to health information seeking, starting with "Who wrote it?" (Author Credentials), followed by "Who published it?" (Institutional Affiliation), "How was it vetted?" (Quality Assurance), and "How does AI find it?" (Digital Authority). This descriptive cross-sectional study randomly selected 100 questions from…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Health Literacy and Information Accessibility · Misinformation and Its Impacts
