Revolutionizing antimicrobial stewardship, infection prevention, and public health with artificial intelligence: the middle path

• Published in: Antimicrobial stewardship & healthcare epidemiology : ASHE Vol. 3; no. 1; p. e219
• Main Authors: Marra, Alexandre R, Langford, Bradley J, Nori, Priya, Bearman, Gonzalo
• Format: Journal Article
• Language: English
• Published: England Cambridge University Press 01.12.2023
• Subjects: Algorithms; Antimicrobial agents; Artificial intelligence; Clinical outcomes; Datasets; Electronic health records; Epidemics; Epidemiology; False information; Health care; Humanitarian aid; Humanitarianism; Infections; Laboratories; Language; Machine learning; Patients; Prevention; Public health
• ISSN: 2732-494X; 2732-494X
• DOI: 10.1017/ash.2023.494

[...]justice must be to offer fair access and to support social justice.2 In this commentary, we explore the application of AI in infection prevention, antimicrobial stewardship, and public health and focus on mitigating its risks (Figure 1). By analyzing patient data and considering factors such as prior antimicrobial use and culture and susceptibility data, AI algorithms further guide clinicians in determining the likelihood of infection, selecting the most appropriate empiric and targeted regimens, provide dose optimization, and minimize the risk of resistance development.7–10 The integration of standard operating procedures, analytic tools, data types, and quality control into a laboratory data warehouse accessed by a large language model will create new possibilities for improving clinical microbiology laboratory practices.11 Additionally, AI can aid in the prediction of antimicrobial resistance patterns directly from mass spectra profiles compared to traditional laboratory-based susceptibility testing.12 Collaboration between healthcare personnel and AI systems requires a mutual understanding of roles and responsibilities. At the patient care level, AI solutions integrated within EHRs, incorporating natural language processing, enable the efficient triage of patients reporting positive results from SARS-CoV-2 tests taken at home. When patients agree to receive health care within our institutions, they are not necessarily consenting to use of this data for purposes outside of individualized patient care.