Investigating the Readability and Quality of AI Systems to Trending Questions About Food Poisoning

TL;DR

This study compares how well AI systems like Google and ChatGPT provide clear and accurate information about food poisoning, finding a trade-off between readability and quality.

Contribution

The paper introduces a novel benchmarking framework to evaluate both readability and quality of AI responses to public health questions.

Findings

Google provided the most readable but lowest quality information on food poisoning.

LLMs like DeepSeek and ChatGPT delivered high-quality information but at higher reading levels.

A trade-off exists between readability and quality in AI systems for public health guidance.

Abstract

Consumers increasingly turn to artificial intelligence (AI) systems, including search engines and large language models (LLMs), for immediate food safety guidance. However, the reliability and accessibility of this information for critical public health issues, such as food poisoning, remain unassessed. This study benchmarks the performance of major AI systems: Google, ChatGPT, DeepSeek, and Mistral, by simultaneously evaluating the readability and information quality of their responses to frequently asked questions on food poisoning. Readability was assessed using the Flesch–Kincaid Grade Level (FKGL), Simple Measure of Gobbledygook (SMOG), and Gunning‐Fog Index (GFI) indices. Information quality was evaluated by independent experts using the validated DISCERN instrument and Global Quality Scale (GQS). Our analysis revealed a critical divergence in platform performance. Google produced the most readable text (FKGL: 9.05) but the lowest quality information (DISCERN: 30–34; GQS: only 3% of ratings were top‐score). Conversely, LLMs provided high‐quality information (DeepSeek DISCERN: 70–75; ChatGPT: 62) but at significantly higher reading levels (FKGL: 10.01–11.32), exceeding the recommended sixth‐grade level. This demonstrates a fundamental trade‐off: search engines optimize for brevity and accessibility, whereas dedicated LLMs prioritize comprehensive, reliable content. This forces consumers to choose between understandable but potentially misleading information and accurate but inaccessible guidance. Our findings highlight an urgent need to bridge this gap between readability and quality, calling for the development of AI systems that deliver authoritative, comprehensible food safety advice to protect public health.

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