ArticleNPJ science of food2026
AI-mediated risks and real-life challenges in mushroom foraging.
Article in NPJ science of food, 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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The rise of AI-based mushroom identification applications impacts foraging despite significant risks from relying solely on AI-generated advice. Popular AI-based mushroom identification tools were tested using 100+ photos of nearly 60 species, taken in real-world conditions. Even the best-performing tool failed in almost 15% of cases, with others performing worse. None of tested applications consistently provided a single, correct answer, demonstrating they cannot be trusted for definitive life-or-death identification decisions.
Identifiers
What Socratic 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 Socratic graph.