Scientists have developed a new diagnostic system to predict antimicrobial resistance (AMR) – the ability of bacteria to resist antibiotics – using artificial intelligence and biochips. The study worked with data from more than 400,000 isolates of the Salmonella enterica bacterium, from which it randomly selected 10,000 samples for testing. The research team created algorithms that analyze genetic signals similar to biochip signals and predict exactly which drugs the bacteria will be resistant to. The proposed classifier achieved high accuracy, precision and other performance metrics across different resistance profiles. The system uses explainable artificial intelligence that identifies key genes responsible for resistance and translates this knowledge into practical recommendations for doctors. This framework combines predictive accuracy with clinically actionable information, offering the potential to improve the diagnosis of antimicrobial resistance in healthcare.