Tran Minh-Hoang, Nguyen Ngoc Quy, Pham Hong Tham
Department of Pharmacy, Nhan Dan Gia Dinh Hospital, Ho Chi Minh City, Vietnam.
Institute of Environmental Technology and Sustainable Development, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam.
Infect Drug Resist. 2022 May 26;15:2685-2688. doi: 10.2147/IDR.S362356. eCollection 2022.
Recent years have witnessed the rise of artificial intelligence (AI) in antimicrobial resistance (AMR) management, implying a positive signal in the fight against antibiotic-resistant microbes. The impact of AI starts with data collection and preparation for deploying AI-driven systems, which can lay the foundation for some effective infection control strategies. Primary applications of AI include identifying potential antimicrobial molecules, rapidly testing antimicrobial susceptibility, and optimizing antibiotic combinations. Aside from their outstanding effectiveness, these applications also express high potential in narrowing the burden gap of AMR among different settings around the world. Despite these benefits, the interpretability of AI-based systems or models remains vague. Attempts to address this issue had led to two novel explanation techniques, but none have shown enough robustness or comprehensiveness to be widely applied in AI and AMR control. A multidisciplinary collaboration between the medical field and advanced technology is therefore needed to partially manage this situation and improve the AI systems' performance and their effectiveness against drug-resistant pathogens, in addition to multiple equity actions for mitigating the failure risks of AI due to a global-scale equity gap.
近年来,人工智能(AI)在抗菌药物耐药性(AMR)管理中兴起,这意味着在对抗抗生素耐药微生物的斗争中出现了积极信号。人工智能的影响始于为部署人工智能驱动系统而进行的数据收集和准备,这可为一些有效的感染控制策略奠定基础。人工智能的主要应用包括识别潜在的抗菌分子、快速检测抗菌药敏性以及优化抗生素组合。除了具有出色的有效性外,这些应用在缩小全球不同地区AMR的负担差距方面也具有很高的潜力。尽管有这些好处,但基于人工智能的系统或模型的可解释性仍然模糊不清。解决这一问题的尝试产生了两种新颖的解释技术,但都没有表现出足够的稳健性或全面性,无法在人工智能和AMR控制中广泛应用。因此,除了采取多项公平行动以减轻由于全球规模的公平差距导致的人工智能失败风险外,还需要医学领域与先进技术之间的多学科合作来部分解决这种情况,并提高人工智能系统的性能及其对抗耐药病原体的有效性。