Can AI Be Used to Treat Infections More Accurately? Study Finds Out - Video

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.

Dr Alex Howard, a consultant in medical microbiology at the University of Liverpool and researcher on the Wellcome Trust funded CAMO-Net said: “This research is important and timely for World AMR Awareness Week because it shows how combining routine health data with lab tests can help keep antibiotics working. By using AI to predict when people with urine infections have antibiotic-resistant bugs, we show how lab tests can better direct their antibiotic treatment. This approach could improve the care of people with infections worldwide and help prevent the spread of antibiotic resistance.”

The results of this study represent a significant step forward in addressing AMR. By prioritising WHO access category antibiotics and tailoring treatment to individual susceptibility profiles, the personalised AST approach not only improves the efficiency of the testing process but also supports global efforts to preserve the effectiveness of critical antibiotics.

Reference: Howard, A., Hughes, D.M., Green, P.L. et al. Personalised antimicrobial susceptibility testing with clinical prediction modelling informs appropriate antibiotic use. Nat Commun 15, 9924 (2024). https://doi.org/10.1038/s41467-024-54192-3