Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation
Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especial...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
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| Series: | Antibiotics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6382/12/3/523 |
| _version_ | 1797613877816459264 |
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| author | Tabish Ali Sarfaraz Ahmed Muhammad Aslam |
| author_facet | Tabish Ali Sarfaraz Ahmed Muhammad Aslam |
| author_sort | Tabish Ali |
| collection | DOAJ |
| description | Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease. |
| first_indexed | 2024-03-11T07:01:54Z |
| format | Article |
| id | doaj.art-c3a517a215f447aa99969b61129fedc5 |
| institution | Directory Open Access Journal |
| issn | 2079-6382 |
| language | English |
| last_indexed | 2024-03-11T07:01:54Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj.art-c3a517a215f447aa99969b61129fedc52023-11-17T09:14:12ZengMDPI AGAntibiotics2079-63822023-03-0112352310.3390/antibiotics12030523Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical ImplementationTabish Ali0Sarfaraz Ahmed1Muhammad Aslam2Department of Civil & Environmental Engineering, Hanyang University, Seoul 04763, Republic of KoreaDepartment of Electronics & Computer Engineering, Hanyang University, Seoul 04763, Republic of KoreaDepartment of Artificial Intelligence, Sejong University, Seoul 05006, Republic of KoreaAntimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.https://www.mdpi.com/2079-6382/12/3/523antimicrobial resistance genesartificial intelligencedeep learningmachine learningchallenges and opportunities |
| spellingShingle | Tabish Ali Sarfaraz Ahmed Muhammad Aslam Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation Antibiotics antimicrobial resistance genes artificial intelligence deep learning machine learning challenges and opportunities |
| title | Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation |
| title_full | Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation |
| title_fullStr | Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation |
| title_full_unstemmed | Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation |
| title_short | Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation |
| title_sort | artificial intelligence for antimicrobial resistance prediction challenges and opportunities towards practical implementation |
| topic | antimicrobial resistance genes artificial intelligence deep learning machine learning challenges and opportunities |
| url | https://www.mdpi.com/2079-6382/12/3/523 |
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