Advancements in AI-driven drug sensitivity testing research DOI Creative Commons

Hongxian Liao,

Lifen Xie,

Nan Zhang

et al.

Frontiers in Cellular and Infection Microbiology, Journal Year: 2025, Volume and Issue: 15

Published: May 2, 2025

Antimicrobial resistance (AMR) constitutes a significant global public health challenge, posing serious threat to human health. In clinical practice, physicians frequently resort empirical antibiotic therapy without timely Susceptibility Testing (AST) results. This however, may induce mutations in pathogens due genetic pressure, thereby complicating infection control efforts. Consequently, the rapid and accurate acquisition of AST results has become crucial for precision treatment. recent years, advancements medical testing technology have led continuous improvements methodologies. Concurrently, emerging artificial intelligence (AI) technologies, particularly Machine Learning(ML) Deep Learning(DL), introduced novel auxiliary diagnostic tools AST. These technologies can extract in-depth information from imaging laboratory data, enabling swift prediction pathogen providing reliable evidence judicious selection antibiotics. article provides comprehensive overview research concerning detection methodologies, emphasizing prospective application machine learning predicting drug sensitivity tests resistance. Furthermore, we anticipate future directions aimed at reducing misuse, enhancing treatment outcomes infected patients, contributing resolution AMR crisis.

Language: Английский

The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review DOI Creative Commons
Flavia Pennisi,

A.C. Pinto,

Giovanni Emanuele Ricciardi

et al.

Antibiotics, Journal Year: 2025, Volume and Issue: 14(2), P. 134 - 134

Published: Jan. 30, 2025

Antimicrobial resistance (AMR) poses a critical global health threat, necessitating innovative approaches in antimicrobial stewardship (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools this domain, enabling data-driven interventions to optimize antibiotic use combat resistance. This comprehensive review explores the multifaceted role of AI ML models enhancing efforts across healthcare systems. AI-powered predictive analytics can identify patterns resistance, forecast outbreaks, guide personalized therapies by leveraging large-scale clinical epidemiological data. algorithms facilitate rapid pathogen identification, profiling, real-time monitoring, precise decision making. These technologies also support development advanced diagnostic tools, reducing reliance on broad-spectrum antibiotics fostering timely, targeted treatments. In public health, AI-driven surveillance systems improve detection AMR trends enhance monitoring capabilities. By integrating diverse data sources—such electronic records, laboratory results, environmental data—ML provide actionable insights policymakers, providers, officials. Additionally, applications programs (ASPs) promote adherence prescribing guidelines, evaluate intervention outcomes, resource allocation. Despite these advancements, challenges such quality, algorithm transparency, ethical considerations must be addressed maximize potential field. Future research should focus developing interpretable interdisciplinary collaborations ensure equitable sustainable integration into initiatives.

Language: Английский

Citations

3
Advancements in AI-driven drug sensitivity testing research DOI Creative Commons

Hongxian Liao,

Lifen Xie,

Nan Zhang

et al.

Frontiers in Cellular and Infection Microbiology, Journal Year: 2025, Volume and Issue: 15

Published: May 2, 2025

Antimicrobial resistance (AMR) constitutes a significant global public health challenge, posing serious threat to human health. In clinical practice, physicians frequently resort empirical antibiotic therapy without timely Susceptibility Testing (AST) results. This however, may induce mutations in pathogens due genetic pressure, thereby complicating infection control efforts. Consequently, the rapid and accurate acquisition of AST results has become crucial for precision treatment. recent years, advancements medical testing technology have led continuous improvements methodologies. Concurrently, emerging artificial intelligence (AI) technologies, particularly Machine Learning(ML) Deep Learning(DL), introduced novel auxiliary diagnostic tools AST. These technologies can extract in-depth information from imaging laboratory data, enabling swift prediction pathogen providing reliable evidence judicious selection antibiotics. article provides comprehensive overview research concerning detection methodologies, emphasizing prospective application machine learning predicting drug sensitivity tests resistance. Furthermore, we anticipate future directions aimed at reducing misuse, enhancing treatment outcomes infected patients, contributing resolution AMR crisis.

Language: Английский

Citations

0