How AI can help us beat AMR

Antimicrobial resistance (AMR) is a pressing public health threat, with the excessive use of antibiotics leading to the development of multidrug-resistant bacteria. This crisis has led to the collection of datasets of diverse bacterial genomes, antibiotic susceptibility testing, and chemical bioactivity screens. AI methods, such as machine learning and deep learning, can handle large data volumes and extract valuable insights from complex datasets. AI-guided decision-making can revolutionize clinical diagnosis, AMR surveillance, and antibiotic discovery. AI algorithms can learn from patient data in electronic health records to support real-time clinical decision-making, while AI-guided surveillance systems can uncover novel resistance mechanisms. AI has also shown potential in identifying new antibiotic candidates by rapidly screening vast chemical libraries and predicting their efficacy and safety. This review explores the integration of AI across clinical diagnostics, AMR surveillance, and antibiotic discovery to mitigate AMR threats and preserve public health.