Antimicrobial resistance represents a critical global health threat, exacerbated by inappropriate empirical antibiotic prescribing. To address this, we developed a Deep Learning-based Clinical Decision Support System trained on over 200,000 historical antibiogram records from three French hospitals to predict antimicrobial resistance and assist clinicians in therapy selection at the bedside, while clinical specimen culture and antimicrobial susceptibility testing are processed at the laboratory. Our model achieves high performance (AUROC of 0.92) and reliable uncertainty calibration (AUSE of 0.03) through Bayesian inference. But clinical deployment faces the persistent challenge of concept drift, as resistance patterns evolve over time. To address this, our present contribution applies the ADWIN algorithm to monitor both error rates and uncertainty signals, enabling detection of concept and virtual drift. This dual-level approach allows proactive identification of emerging resistant strains and ensures the long-term safety and reliability of the system in dynamic clinical settings.