Enhancing antimicrobial resistance strategies: Leveraging artificial intelligence for improved outcomes
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, di...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | South African Journal of Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1026918524001446 |
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| author | Aeshah M. Mohammed Mohammed Mohammed Jawad K. Oleiwi Azlin F. Osman Tijjani Adam Bashir O. Betar Subash C.B. Gopinath Falah H. Ihmedee |
| author_facet | Aeshah M. Mohammed Mohammed Mohammed Jawad K. Oleiwi Azlin F. Osman Tijjani Adam Bashir O. Betar Subash C.B. Gopinath Falah H. Ihmedee |
| author_sort | Aeshah M. Mohammed |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e964169bb237494dabd9149548df4969 |
| institution | Kabale University |
| issn | 1026-9185 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | South African Journal of Chemical Engineering |
| spelling | doaj-art-e964169bb237494dabd9149548df49692025-01-19T06:24:20ZengElsevierSouth African Journal of Chemical Engineering1026-91852025-01-0151272286Enhancing antimicrobial resistance strategies: Leveraging artificial intelligence for improved outcomesAeshah M. Mohammed0Mohammed Mohammed1Jawad K. Oleiwi2Azlin F. Osman3Tijjani Adam4Bashir O. Betar5Subash C.B. Gopinath6Falah H. Ihmedee7University of Bagdad College of Education for Pure Science Ibn-Alhaitham, Baghdad, 10001, IraqCenter of Excellence Geopolymer & Green Technology (CEGeoGTech), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia; Faculty of Chemical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Arau, 02600, Perlis, Malaysia; Corresponding author.Department of Materials Engineering, University of Technology, Baghdad, IraqCenter of Excellence Geopolymer & Green Technology (CEGeoGTech), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia; Faculty of Chemical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Arau, 02600, Perlis, MalaysiaFaculty of Electronics Engineering Technology, Universiti Malaysia Perlis, Kampus Uniciti Alam Sg. Chuchuh, 02100, Padang Besar (U), Perlis, MalaysiaResearch Center (NANOCAT), University of Malaya, Kuala Lumpur, 50603, MalaysiaFaculty of Chemical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Arau, 02600, Perlis, Malaysia; Institute of Nano Electronic Engineering, Universiti Malaysia Perlis, Perlis, Malaysia; Center for Global Health Research, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602 105, Tamil Nadu, IndiaMinistry of Industry and Mineral corporation of research and industrial development, IbnAlbetar center, Baghdad, IraqAntimicrobial 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.http://www.sciencedirect.com/science/article/pii/S1026918524001446Artificial intelligenceAntimicrobial resistanceAI in healthcareAntibiotic discoveryPredictive analytics |
| spellingShingle | Aeshah M. Mohammed Mohammed Mohammed Jawad K. Oleiwi Azlin F. Osman Tijjani Adam Bashir O. Betar Subash C.B. Gopinath Falah H. Ihmedee Enhancing antimicrobial resistance strategies: Leveraging artificial intelligence for improved outcomes South African Journal of Chemical Engineering Artificial intelligence Antimicrobial resistance AI in healthcare Antibiotic discovery Predictive analytics |
| title | Enhancing antimicrobial resistance strategies: Leveraging artificial intelligence for improved outcomes |
| title_full | Enhancing antimicrobial resistance strategies: Leveraging artificial intelligence for improved outcomes |
| title_fullStr | Enhancing antimicrobial resistance strategies: Leveraging artificial intelligence for improved outcomes |
| title_full_unstemmed | Enhancing antimicrobial resistance strategies: Leveraging artificial intelligence for improved outcomes |
| title_short | Enhancing antimicrobial resistance strategies: Leveraging artificial intelligence for improved outcomes |
| title_sort | enhancing antimicrobial resistance strategies leveraging artificial intelligence for improved outcomes |
| topic | Artificial intelligence Antimicrobial resistance AI in healthcare Antibiotic discovery Predictive analytics |
| url | http://www.sciencedirect.com/science/article/pii/S1026918524001446 |
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