The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance
Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natu...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025000078 |
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| author | Hazrat Bilal Muhammad Nadeem Khan Sabir Khan Muhammad Shafiq Wenjie Fang Rahat Ullah Khan Mujeeb Ur Rahman Xiaohui Li Qiao-Li Lv Bin Xu |
| author_facet | Hazrat Bilal Muhammad Nadeem Khan Sabir Khan Muhammad Shafiq Wenjie Fang Rahat Ullah Khan Mujeeb Ur Rahman Xiaohui Li Qiao-Li Lv Bin Xu |
| author_sort | Hazrat Bilal |
| collection | DOAJ |
| description | Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR. |
| format | Article |
| id | doaj-art-69d1c732dc564e1eb4dd0866c49ac6b8 |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-69d1c732dc564e1eb4dd0866c49ac6b82025-01-23T05:26:35ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-0127423439The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistanceHazrat Bilal0Muhammad Nadeem Khan1Sabir Khan2Muhammad Shafiq3Wenjie Fang4Rahat Ullah Khan5Mujeeb Ur Rahman6Xiaohui Li7Qiao-Li Lv8Bin Xu9Jiangxi Key Laboratory of oncology (2024SSY06041), JXHC Key Laboratory of Tumour Metastasis, NHC Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma, Jiangxi Cancer Hospital & Institute, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi 330029, PR ChinaDepartment of Cell Biology and Genetics, Shantou University Medical College, Shantou 515041, ChinaDepartment of Dermatology, The Second Affiliated Hospital of Shantou University Medical College, Shantou 515041, ChinaResearch Institute of Clinical Pharmacy, Department of Pharmacology, Shantou University Medical College, Shantou 515041, ChinaDepartment of Dermatology, Changzheng Hospital, Second Military Medical University, Shanghai 200003, ChinaCollege of Life Sciences, University of Chinese Academy of Sciences, Beijing 101408, China; CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Center for Influenza Research and Early-warning (CASCIRE), CAS-TWAS Center of Excellence for Emerging Infectious Diseases (CEEID), Chinese Academy of Sciences, Beijing 100101, ChinaBiofuels Institute, School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, ChinaJiangxi Key Laboratory of oncology (2024SSY06041), JXHC Key Laboratory of Tumour Metastasis, NHC Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma, Jiangxi Cancer Hospital & Institute, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi 330029, PR ChinaJiangxi Key Laboratory of oncology (2024SSY06041), JXHC Key Laboratory of Tumour Metastasis, NHC Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma, Jiangxi Cancer Hospital & Institute, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi 330029, PR China; Correspondence to: Jiangxi Key Laboratory of Oncology (2024SSY06041), JXHC Key Laboratory of Tumor Metastasis, Jiangxi Cancer Hospital & Institute, 519 Beijing East Road, Nanchang, Jiangxi 330029, China.Jiangxi Key Laboratory of oncology (2024SSY06041), JXHC Key Laboratory of Tumour Metastasis, NHC Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma, Jiangxi Cancer Hospital & Institute, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi 330029, PR China; Correspondence to: Jiangxi Key Laboratory of Oncology (2024SSY06041), JXHC Key Laboratory of Tumor Metastasis, Jiangxi Cancer Hospital & Institute, 519 Beijing East Road, Nanchang, Jiangxi 330029, China.Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.http://www.sciencedirect.com/science/article/pii/S2001037025000078Antimicrobial ResistanceArtificial IntelligenceMachine Learning, Drug DiscoveryAMR surveillance |
| spellingShingle | Hazrat Bilal Muhammad Nadeem Khan Sabir Khan Muhammad Shafiq Wenjie Fang Rahat Ullah Khan Mujeeb Ur Rahman Xiaohui Li Qiao-Li Lv Bin Xu The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance Computational and Structural Biotechnology Journal Antimicrobial Resistance Artificial Intelligence Machine Learning, Drug Discovery AMR surveillance |
| title | The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance |
| title_full | The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance |
| title_fullStr | The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance |
| title_full_unstemmed | The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance |
| title_short | The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance |
| title_sort | role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance |
| topic | Antimicrobial Resistance Artificial Intelligence Machine Learning, Drug Discovery AMR surveillance |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025000078 |
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