Background: Artificial intelligence (AI) has the potential to enhance clinical decision-making, including in infectious diseases. By improving antimicrobial resistance prediction and optimizing antibiotic prescriptions, these technologies may support treatment strategies and address critical gaps in healthcare. This study evaluates the effectiveness of AI in guiding appropriate antibiotic prescriptions for infectious diseases through a systematic literature review. Methods: We conducted a systematic review of studies evaluating AI (machine learning or large language models) used for guidance on prescribing appropriate antibiotics in infectious disease cases. Searches were performed in PubMed, CINAHL, Embase, Scopus, Web of Science, and Google Scholar for articles published up to October 25, 2024. Inclusion criteria focused on studies assessing the performance of AI in clinical practice, with outcomes related to antimicrobial management and decision-making. Results: Seventeen studies used machine learning as part of clinical decision support systems (CDSS). They improved prediction of antimicrobial resistance and optimized antimicrobial use. Six studies focused on large language models to guide antimicrobial therapy; they had higher prescribing error rates, patient safety risks, and needed precise prompts to ensure accurate responses. Conclusions: AI, particularly machine learning integrated into CDSS, holds promise in enhancing clinical decision-making and improving antimicrobial management. However, large language models currently lack the reliability required for complex clinical applications. The indispensable role of infectious disease specialists remains critical for ensuring accurate, personalized, and safe treatment strategies. Rigorous validation and regular updates are essential before the successful integration of AI into clinical practice.

BACKGROUND: An mpox (formerly “monkeypox”) outbreak began in 2022, leading to infection in special populations, including pregnant individuals. CASE: We present a case of an individual who presented with a labial ulcer and subsequent papular rash at 31 weeks of gestation. She was diagnosed with mpox infection and was treated with tecovirimat. She had an uncomplicated induction of labor at 39 2/7 weeks of gestation and delivered a healthy neonate. The neonate had a positive immunoglobulin G test result for orthopoxvirus but did not have skin lesions or positive molecular test results suggestive of infection. CONCLUSION: Transplacental transmission of mpox is possible, but, in this case, the neonate did not have clinical findings suggestive of active or antenatal mpox infection. Treatment with tecovirimat in gestational cases of mpox may be beneficial.

Single-use surgical equipment is a standard strategy to reduce the risk of pathogen transmission in the operative room. However, this practice is associated with a great environmental impact. Reusable surgical tools represent an opportunity to reduce this impact, with many studies showing a 50% or greater reduction in carbon emissions by switching to reusable alternatives. While the safety of reusable equipment depends on strict sterilization protocols, the risk of infection is minimal when guidelines are followed. To advance sustainability in healthcare, we must balance infection prevention priorities, operational challenges, and the environmental considerations.

We evaluated large language model (LLM)-based agents integrated with the electronic medical record to assess blood culture appropriateness. While sensitivity was high, specificity remained low. Performance was shaped by prompt phrasing, sycophantic behavior, and semantic triggers, reflecting both the potential and limitations of LLMs in real-world clinical decision support.