Artificial Intelligence (AI) has helped identify knowledge, methodological and communication gaps in global Antimicrobial Resistance (AMR) research.
May 17 2024Newcastle University Artificial Intelligence has helped identify knowledge, methodological and communication gaps in global Antimicrobial Resistance research.
They found that the terminology and methods used in AMR research significantly differ across the medical, veterinary, food safety, plant agriculture, and environmental sectors. The semantic and methodological differences result in limited valuation work between sectors and limited cross-sectoral communication, resulting in inconsistent messages to decision-makers.
Published in the journal Environment International, the findings explain why solutions to AMR based on One Health are not developing as needed. The results could play a critical role in providing guidance on how and where to better integrate AMR surveillance across sectors and regions worldwide. "Our paper's findings support key messages from UN Environment Programme and World Health Organization that emphasize the best way to mitigate AMR is through prevention and integrated surveillance, which is key to prioritizing solutions."
Artificial Intelligence Research Agriculture Food Food Safety Veterinary
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