The overuse and misuse of antibiotics increases antibiotic resistance (AMR), limiting treatment options and placing a major economic and clinical burden on healthcare systems (Ribers & Ullrich, 2019) (Price, 2016). According to WHO/ECDC's 2023 report, resistance to broad-spectrum beta-lactam antibiotics has reached critical levels in Europe. In the United States, more than 2.8 million resistant infections occur each year, and more than 35,000 people die. If no measures are taken, it is estimated that AMR-related deaths will reach 10 million annually by 2050. Against this threat, artificial intelligence (AI)-based clinical decision support systems (CDSSs) offer an innovative approach to optimise antibiotic prescribing processes. By analysing patient data, microbial profiles, and local antibiotic resistance patterns, personalised and targeted antibiotic recommendations can be provided (Ribers & Ullrich, 2019) (Tran et al., 2022). However, AI-assisted prescribing systems should not only improve the technical efficiency, but also comply with ethical principles. The utilitarian approach is proposed as the most appropriate ethical framework, as it aims to minimise the development of resistance while maximising patient benefit (Bolton et al., 2022). WHO launched the ‘Antimicrobial Resistance (AMD) Call to Action’ in 2021, accelerating the global response with the participation of 113 countries and 38 organisations. With the lessons of COVID-19, the WHO and the UN have identified AMR as a ‘Silent Tsunami’ and committed to take measures under the 2030 Agenda for Sustainable Development. As a result, AI-based strategies have great potential in combating antibiotic resistance (Pascucci et al., 2021). AI-powered systems can protect public health by reducing unnecessary antibiotic use. However, transparency, ethical standards, and regulatory frameworks are required for its successful implementation. Future research can contribute to sustainable antibiotic stewardship by improving the accuracy of AI models.