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Superbacteria: paradox and risk of modern medicine but AI lends a hand

Resistant bacteria pose a silent and real threat: we need an integrated strategy combining pharmacological innovation, appropriate use of antibiotics, advanced epidemiological surveillance and intelligent digital tools

by Marco Falcone *

1 April 2026

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3' min read

Translated by AI

Versione italiana

3' min read

Translated by AI

Versione italiana

Modern medicine today faces a paradox that marks one of the most complex challenges of the coming years: while scientific innovation has made it possible to control diseases that were once lethal, biological evolution itself has favoured the emergence of bacteria that are increasingly resistant to antibiotics. So-called 'superbugs' represent a growing threat, capable of undermining healthcare systems and slowing down therapeutic advances, particularly in highly fragile areas such as oncology.

Risks in Oncology

Therapeutic advances in oncology are turning many neoplasms into increasingly controllable diseases, significantly prolonging survival. But this advance comes up against a structural fragility: treatment-related immunosuppression exposes patients to serious infections. In the presence of multi-resistant bacteria, the risk becomes real and immediate, to the point of jeopardising the very benefits obtained from cancer therapies. In other words, you live longer thanks to oncological innovation, but you die from a bacterial infection that is difficult to treat.

Antibiotics in slow motion

At the same time, the development of new antibiotics is proceeding slowly. The pipeline has progressively shrunk due to a combination of factors: increasing scientific complexity, long development times and the difficulty of identifying molecules effective against highly adaptable bacteria. In this context, artificial intelligence (AI) is emerging as a possible turning point, offering tools that can speed up the identification of new compounds and make the research pipeline more efficient.

The perspectives of AI

AI offers new perspectives in both drug discovery and clinical management of infections. On the research front, advanced algorithms are capable of analysing huge amounts of molecular data to identify possible new antibiotic molecules, speeding up processes that traditionally take years. However, it is in everyday clinical practice that the impact of AI already appears concrete: decision support systems can help physicians choose the most appropriate therapy, reducing diagnosis time and increasing the precision of interventions.

Extended surveillance

In this context, infection monitoring plays a central role. Modern management can no longer be confined to infectious disease departments, but must cut across all levels of the healthcare system. Protecting the health of the population today means integrating epidemiological surveillance with technological innovation, turning every piece of clinical data into a prevention tool.

A concrete example of this approach is Resistimit, the national project promoted by the Italian Society of Infectious and Tropical Diseases (Simit). It is a clinical network that connects over 55 Italian hospitals and collects real-time data on infections with multi-resistant bacteria. To date, some 2,700 cases of serious infections have been analysed, with a 30-day mortality rate of 20-25% for resistant Klebsiella strains and over 40% for Acinetobacter baumannii. These numbers not only represent an alarm, but also a fundamental knowledge base.

Systematic data collection makes it possible to identify at-risk patients more quickly, guide therapeutic choices and develop increasingly accurate predictive models, also thanks to integration with artificial intelligence tools. In other words, each clinical case becomes a piece of a larger system, capable of learning and improving over time.

Anti-pandemic Strategy

The international scientific community is wondering what the emerging pandemics of the coming years will be: resistant bacteria represent a silent and concrete threat, less visible than emerging viruses, but potentially just as devastating. The response to this challenge cannot be based on a single approach. We need an integrated strategy that combines pharmacological innovation, appropriate use of antibiotics, advanced epidemiological surveillance and intelligent digital tools. Artificial intelligence, in this framework, does not replace the doctor, but enhances his capabilities, enabling faster and more informed decisions.

The real revolution will not only be technological, but cultural: moving from reactive medicine to predictive medicine, capable of anticipating infectious risk before it manifests itself. In an increasingly interconnected world, where pathogens know no geographical boundaries, the ability to transform data into knowledge is the first line of defence.

* Professor of Infectious Diseases at the University of Pisa and Director of the U.O. of Infectious Diseases Azienda Ospedaliera Universitaria Pisana