<span style="mso-fareast-font-family: 'Palatino Linotype'; background: white; mso-highlight: white;">AI-Based Treatment Recommendations Enhance Speed and Accuracy in Bacteremia Management: A Comparative Study of Molecular and Phenotypic Data<span style="font-size: 13.0pt;">

Background: Bloodstream infections remain a critical global health concern due to their high morbidity and mortality, compounded by increasing antimicrobial resistance and delays in initiating targeted therapy. This study evaluates the effectiveness and timeliness of therapeutic recommendations generated by an artificial intelligence (AI)-driven clinical decision support system (CDSS), comparing outputs based solely on molecular diagnostics versus those integrating molecular and phenotypic data. Methods: In a prospective cross-sectional study conducted in Lima, Peru, 117 blood cultures were analyzed using FilmArray/GeneXpert for molecular identification and MALDI-TOF/VITEK 2.0 for phenotypic profiling. The AI-based CDSS provided treatment recommendations in two formats, which were assessed for concordance and turnaround time. Results: Therapeutic recommendations showed 80.3% consistency between data types, with 86.3% concordance in pathogen and resistance detection. Notably, molecular-only recommendations were delivered 29 hours earlier than those incorporating phenotypic data. Escherichia coli was the most frequently isolated pathogen, with a 95% concordance in suggested therapy. A substantial agreement was observed in treatment consistency (Kappa = 0.80). Conclusions: These findings highlight the potential of AI-powered molecular diagnostics to accelerate clinical decision-making in bacteremia, supporting more timely interventions and improved antimicrobial stewardship. Further research is warranted to assess scalability and impact across diverse clinical settings.