Data limitations hinder the development of AI-based decision support for the treatment of antibiotic-resistant bacteria

A. Johnning, S. Käll, E. Kristiansson. Future Microbiology, 1–3. Online 10 October 2025.

Abstract

Our ability to treat and prevent bacterial infections with antibiotics is a cornerstone of modern healthcare. Diseases caused by antibiotic-resistant bacteria are, therefore, recognized as one of the most serious threats to human health worldwide. When treating an infected patient, time is often of the essence, as quickly administering an effective drug lowers the risk of transmission, prolonged illness, and death. Ideally, the selected drug should also precisely target the causative pathogen to minimize the selection for resistance to broad-spectrum antibiotics.

Diagnostics provide the treating physician with vital information for choosing the most suitable therapy. However, the urgency of life-threatening infections may require immediate action before complete diagnostic results are available. This practice, a form of “empirical treatment”, increases patient mortality and morbidity and leads to unnecessary antibiotic use [Citation1]. The WHO has therefore highlighted that timely and accurate diagnosis is one of the pillars in tackling the resistance crisis [Citation2].




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