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A comparative analysis of CDC and AI-generated health information using computer-aided text analysis.
1Central Connecticut State University, New Britain, CT, United States.
AI-generated health information often uses more negative sentiment and is harder to read than content from the Centers for Disease Control and Prevention (CDC). Public health education should address AI content quality and readability for better health literacy.
Area of Science:
- Digital Health
- Health Communication
- Artificial Intelligence in Healthcare
Background:
- Public access to AI-generated content offers an alternative or supplement to official health information sources like the CDC.
- The reliability and quality of AI-generated health information remain a significant concern.
- Language Expectancy Theory provides a framework for understanding how source expectations influence perceived credibility.
Purpose of the Study:
- To compare AI-generated health information with CDC-provided information.
- To analyze differences in sentiment, readability, and overall quality.
- To evaluate AI-generated content against established health information standards.
Main Methods:
- Computer-aided text analysis of 20 CDC entries and 20 ChatGPT 3.5 entries.
- Human coders conducted content analysis to assess information quality.
- Quantitative analysis of sentiment, readability scores, and DISCERN scores.
Main Results:
- ChatGPT exhibited more negative sentiment (anger, sadness, disgust) compared to CDC content.
- CDC messages were significantly easier to read and required a lower grade level than ChatGPT responses.
- The CDC's information demonstrated higher overall quality, evidenced by superior DISCERN scores.
Conclusions:
- Public health professionals must educate the public on the nuances of AI-generated health information quality and readability.
- Health literacy initiatives should incorporate critical evaluation of AI content.
- Recommendations are provided for the responsible use of AI in health information dissemination.
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