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.
Data availability
All data has been included in this article and the supplemental material section.
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Acknowledgements
The authors thank the IMC12 Congress Organizing Committee for interesting discussions and Prof. Dr. Zhu-Liang Yang at the Kunming Institute of Botany (Chinese Academy of Sciences) for invaluable assistance. This study was supported by ASPIRE, the technology program management pillar of Abu Dhabi’s Advanced Technology Research Council (ATRC), via the ASPIRE Precision Medicine Research Institute Abu Dhabi (ASPIREPMRIAD) award grant number VRI-20-10.
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N.V.K. conceptualized, designed and supervised the study. N.V.K., Y.X., and S.K. acquired the data, performed investigation and conducted analyses. N.V.K. wrote the draft of manuscript and prepared the figures for data presentation. F.C.K. contributed to the literature review and editing of the pre-submitted manuscript. N.V.K., F.C.K., and M.L. edited the final version of the manuscript. M.L. secured funding, provided supervision, and managed project administration. All authors approved the submitted version.
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Kuznetsov, N.V., Xia, Y., Kuznetsov, S. et al. AI-mediated risks and real-life challenges in mushroom foraging. npj Sci Food (2026). https://doi.org/10.1038/s41538-026-00752-4
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DOI: https://doi.org/10.1038/s41538-026-00752-4
