Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis
Advances in high-throughput technologies, digital phenotyping, and increased accessibility of publicly available datasets offer opportunities for big data to be applied in infectious disease surveillance, diagnosis, treatment, and outcome prediction. Artificial intelligence (AI) and machine learning...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/1999-4915/17/7/882 |
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| author | Brandon C. J. Cheah Creuza Rachel Vicente Kuan Rong Chan |
| author_facet | Brandon C. J. Cheah Creuza Rachel Vicente Kuan Rong Chan |
| author_sort | Brandon C. J. Cheah |
| collection | DOAJ |
| description | Advances in high-throughput technologies, digital phenotyping, and increased accessibility of publicly available datasets offer opportunities for big data to be applied in infectious disease surveillance, diagnosis, treatment, and outcome prediction. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to analyze complex clinical and molecular data. However, it remains unclear which AI or ML models are most suitable for infectious disease management, as most existing studies use non-scoping literature reviews to recommend AI and ML models for data analysis. This scoping literature review thus examines the ML models and applications that are most relevant for infectious disease management, with a proposed actionable workflow for implementing ML models in clinical practice. We conducted a literature search on PubMed, Google Scholar, and ScienceDirect, including papers published in English between January 2020 and April 2024. Search keywords included AI, ML, public health, surveillance, diagnosis, prognosis, and infectious disease, to identify published studies using AI and ML in infectious disease management. Studies without public datasets or lacking descriptions of the ML models were excluded. This review included a total of 77 studies applied in surveillance, prognosis, and diagnosis. Different types of input data from infectious disease surveillance, clinical diagnosis, and prognosis required different ML and AI models to achieve the maximum performance in infectious disease management. Our findings highlight the potential of Explainable AI and ensemble learning models to be more broadly applicable in different aspects of infectious disease management, which can be integrated in clinical workflows to improve infectious disease surveillance, diagnosis, and prognosis. Explainable AI and ensemble learning models can be suitably used to achieve high accuracy in prediction. However, as most of the studies have not been validated in different cohorts, it remains unclear whether these ML models can be broadly applicable to different populations. Nonetheless, the findings encourage deploying ML and AI to complement clinicians and augment clinical decision-making. |
| format | Article |
| id | doaj-art-5f80af00b2af4b7593868b1f6d5acf05 |
| institution | Kabale University |
| issn | 1999-4915 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj-art-5f80af00b2af4b7593868b1f6d5acf052025-08-20T03:56:47ZengMDPI AGViruses1999-49152025-06-0117788210.3390/v17070882Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and PrognosisBrandon C. J. Cheah0Creuza Rachel Vicente1Kuan Rong Chan2Program in Emerging Infectious Diseases, Duke-NUS Medical School, 8 College Road, Singapore 169857, SingaporeDepartamento de Saúde Coletiva, Universidade Federal do Espírito Santo, Vitória 29090-040, Espírito Santo, BrazilProgram in Emerging Infectious Diseases, Duke-NUS Medical School, 8 College Road, Singapore 169857, SingaporeAdvances in high-throughput technologies, digital phenotyping, and increased accessibility of publicly available datasets offer opportunities for big data to be applied in infectious disease surveillance, diagnosis, treatment, and outcome prediction. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to analyze complex clinical and molecular data. However, it remains unclear which AI or ML models are most suitable for infectious disease management, as most existing studies use non-scoping literature reviews to recommend AI and ML models for data analysis. This scoping literature review thus examines the ML models and applications that are most relevant for infectious disease management, with a proposed actionable workflow for implementing ML models in clinical practice. We conducted a literature search on PubMed, Google Scholar, and ScienceDirect, including papers published in English between January 2020 and April 2024. Search keywords included AI, ML, public health, surveillance, diagnosis, prognosis, and infectious disease, to identify published studies using AI and ML in infectious disease management. Studies without public datasets or lacking descriptions of the ML models were excluded. This review included a total of 77 studies applied in surveillance, prognosis, and diagnosis. Different types of input data from infectious disease surveillance, clinical diagnosis, and prognosis required different ML and AI models to achieve the maximum performance in infectious disease management. Our findings highlight the potential of Explainable AI and ensemble learning models to be more broadly applicable in different aspects of infectious disease management, which can be integrated in clinical workflows to improve infectious disease surveillance, diagnosis, and prognosis. Explainable AI and ensemble learning models can be suitably used to achieve high accuracy in prediction. However, as most of the studies have not been validated in different cohorts, it remains unclear whether these ML models can be broadly applicable to different populations. Nonetheless, the findings encourage deploying ML and AI to complement clinicians and augment clinical decision-making.https://www.mdpi.com/1999-4915/17/7/882machine learningartificial intelligenceinfectious diseases managementsurveillancediagnosisprognosis |
| spellingShingle | Brandon C. J. Cheah Creuza Rachel Vicente Kuan Rong Chan Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis Viruses machine learning artificial intelligence infectious diseases management surveillance diagnosis prognosis |
| title | Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis |
| title_full | Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis |
| title_fullStr | Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis |
| title_full_unstemmed | Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis |
| title_short | Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis |
| title_sort | machine learning and artificial intelligence for infectious disease surveillance diagnosis and prognosis |
| topic | machine learning artificial intelligence infectious diseases management surveillance diagnosis prognosis |
| url | https://www.mdpi.com/1999-4915/17/7/882 |
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