Enhancing antimicrobial resistance strategies: Leveraging artificial intelligence for improved outcomes - Aeshah M. Mohammed; Mohammed Mohammed; Jawad K. Oleiwi; Azlin F. Osman; Tijjani Adam; Bashir O. Betar; Subash C.B. Gopinath; Falah H. Ihmedee (South African Journal of Chemical Engineering)
Antimicrobial resistance (AMR) poses a formidable challenge to global health, threatening to undermine the efficacy of antibiotics and jeopardize medical advances. Despite concerted efforts to combat AMR, traditional strategies often fall short, necessitating innovative approaches to stewardship, diagnosis, and treatment. This review explores the burgeoning role of artificial intelligence (AI) in revolutionizing AMR strategies, offering a beacon of hope for turning the tide against resistant pathogens. By synthesizing current research and applications, the potential of AI-driven technologies—ranging from machine learning models that predict resistance patterns to algorithms enhancing antibiotic discovery—is illuminated to augment our arsenal against AMR. Furthermore, the successes and limitations of these technologies are critically examined, navigating through the complexities of AI integration into healthcare settings. Despite facing challenges such as data privacy concerns and the need for robust regulatory frameworks, AI holds promise for significantly improving AMR outcomes. Through a forward-looking lens, future prospects for AI in mitigating AMR are discussed, emphasizing the importance of interdisciplinary collaboration and innovation in healthcare strategies. This review not only highlights AI's potential to enhance AMR management but also calls for a concerted effort to harness its capabilities, thereby safeguarding the efficacy of antimicrobial agents and ensuring a sustainable healthcare future.