A focused online session brought together clinical, scientific and technological expertise to present the AntiMO project and discuss how data and predictive models can support more informed healthcare decisions.
As part of the AI4VET4AI (AI-Powered Next Generation of VET) project activities, the online event “AI and Precision Medicine to Fight Antibiotic Resistance” took place on Microsoft Teams on 16 July 2026. Bringing together experts from Kelyon S.r.l., IDI IRCCS and the University of Salerno, the session provided an opportunity to present and discuss the AntiMO project and explore how clinical data, microbiology insights and predictive models can contribute to more informed approaches to antibiotic resistance. By fostering dialogue between clinical, scientific and technological perspectives, the event also reflected AI4VET4AI’s wider commitment to connecting different stakeholders and raising awareness of the practical applications of artificial intelligence through knowledge-sharing and interdisciplinary cooperation.
The event opened with an introduction to AntiMO by Stefano Tagliaferri, CTO at Kelyon, who outlined the project, its objectives and the wider context in which the initiative is being developed. His contribution set the scene for a multidisciplinary discussion on how artificial intelligence and precision medicine can help transform complex healthcare data into practical support for clinical decision-making.
Giuseppe Piccinni, Head of the Clinical Laboratory Unit at IDI IRCCS, explored the role of microbiology data and local resistance patterns. His presentation highlighted the importance of local epidemiology as a foundation for better clinical and organisational decisions, especially in contexts where antibiotic resistance can vary significantly across settings and patient populations.
Antonio Facchiano, MD and Oncologist at IDI IRCCS, then focused on the transition from antibiotic resistance to personalised treatment decisions. His contribution emphasised the clinical relevance of data-driven approaches and the potential value of integrating different types of information to support more targeted and appropriate therapeutic choices.
The technological perspective was presented by Giuseppe Loffredo, Data Scientist at Kelyon, who discussed how clinical data and predictive models can contribute to the development of advanced decision-support tools. By combining healthcare information with analytical methods, projects such as AntiMO aim to provide clinicians with additional evidence to inform decisions in complex cases.
The session concluded with Roberto Tagliaferri, Full Professor at the University of Salerno, who presented key results and future perspectives, connecting the scientific and technological elements discussed throughout the event. The event was designed as a focused knowledge-sharing opportunity and encouraged dialogue between clinical, scientific and technological perspectives. It demonstrated the value of interdisciplinary collaboration in addressing antibiotic resistance and showed how initiatives such as AntiMO can contribute to more precise, data-informed and effective healthcare decision-making.
ABOUT THE PROJECT
AI4VET4AI – AI-powered Next Generation of VET – brings together partners from across Europe to strengthen artificial intelligence skills, foster cooperation between education, research and business, and support innovative AI-related learning and knowledge-sharing activities. The project is supported by the European Union through the Erasmus+ Partnership for Excellence – Centres of Vocational Excellence programme.
