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Life sciences · Journal article
Artefactum · October 13, 2026
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The global emergence of multidrug-resistant Gram-negative bacilli represents one of the major global public health challenges today, requiring structured interventions for antimicrobial stewardship. The guidelines of the Brazilian Society of Infectious Diseases (SBI) establish rigorous, evidence-based therapeutic protocols for the treatment of severe infections caused by carbapenemase-producing Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter baumannii. The SBI emphasizes the selection of initial empirical coverage grounded in local epidemiology and microbiological profiles, the optimization of pharmacokinetic and pharmacodynamic parameters, as well as mandatory reassessment at 48 to 72 hours to promote early therapeutic de-escalation. This article presents an expanded narrative and critical-analytical review on the role of Artificial Intelligence (AI) as a clinical decision support tool to enable and operationalize the implementation of SBI guidelines at the hospital bedside. The study delves into the main applications of AI, including individualized prediction of resistance risk upon admission, accelerated predictive antibiograms via mass spectrometry and deep learning, real-time precision dosing, and natural language processing for auditing narrative records in electronic health records, critically contrasting them with the reasons justifying the absence of AI in official protocols of other international medical organizations and the SBI itself. From the perspective of Brazilian regulation, the study analyzes in detail compliance with the General Data Protection Law (LGPD) and Resolution CFM No. 2,454/2026 of the Federal Council of Medicine, which categorically prohibits prescriptive autonomy for automated systems, ensuring the centrality of medical judgment and enshrining the 'human-in-the-loop' paradigm.