ArticleJournal of food science2026
Investigating the Readability and Quality of AI Systems to Trending Questions About Food Poisoning.
Article in Journal of food science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.