Can AI Be Used to Treat Infections More Accurately?

#artificialintelligence #urinarytractinfection #naturecommunications

New research from the Centres for Antimicrobial Optimisation Network (CAMO-Net) at the University of Liverpool has shown that using artificial intelligence (AI) can improve how we treat urinary tract infections (UTIs), and help to address antimicrobial resistance (AMR).
Traditional UTI diagnostic tests, known as antimicrobial susceptibility testing (AST), uses a one-size-fits-all approach to determine which antibiotics are most effective against a specific bacterial or fungal infection. This new research, published in Nature Communications, proposes a personalised method, using real-time data to help clinicians target infections more accurately and reduce the chance of bacteria becoming resistant to antibiotic treatment.
The researchers used AI to test prediction models for 12 antibiotics using real patient data and compared personalised AST with standard methods. The data-driven personalised approach led to more accurate treatment options, especially with WHO Access antibiotics, known for being less likely to cause resistance.
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