The Use of Artificial Intelligence to Analyze the Exposome in the Development of Chronic Diseases: A Review of the Current Literature
The “Exposome” is a concept that indicates the set of exposures to which a human is subjected during their lifetime. These factors influence the health state of individuals and can drive the development of Noncommunicable Diseases (NCDs). Artificial Intelligence (AI) allows one to analyze large amou...
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2024-11-01
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| author | Stefania Isola Giuseppe Murdaca Silvia Brunetto Emanuela Zumbo Alessandro Tonacci Sebastiano Gangemi |
| author_facet | Stefania Isola Giuseppe Murdaca Silvia Brunetto Emanuela Zumbo Alessandro Tonacci Sebastiano Gangemi |
| author_sort | Stefania Isola |
| collection | DOAJ |
| description | The “Exposome” is a concept that indicates the set of exposures to which a human is subjected during their lifetime. These factors influence the health state of individuals and can drive the development of Noncommunicable Diseases (NCDs). Artificial Intelligence (AI) allows one to analyze large amounts of data in a short time. As such, several authors have used AI to study the relationship between exposome and chronic diseases. Under such premises, this study reviews the use of AI in analyzing the exposome to understand its role in the development of chronic diseases, focusing on how AI can identify patterns in exposure-related data and support prevention strategies. To achieve this, we carried out a search on multiple databases, including PubMed, ScienceDirect, and SCOPUS, from 1 January 2019 to 31 May 2023, using the MeSH terms (exposome) and (‘Artificial Intelligence’ OR ‘Machine Learning’ OR ‘Deep Learning’) to identify relevant studies on this topic. After completing the identification, screening, and eligibility assessment, a total of 18 studies were included in this literature review. According to the search, most authors used supervised or unsupervised machine learning models to study multiple exposure factors’ role in the risk of developing cardiovascular, metabolic, and chronic respiratory diseases. In some more recent studies, authors also used deep learning. Furthermore, the exposome analysis is useful to study the risk of developing neuropsychiatric disorders or evaluating pregnancy outcomes and child growth. Understanding the role of the exposome is pivotal to overcome the classic concept of a single exposure/disease. The application of AI allows one to analyze multiple environmental risks and their combined effects on health conditions. In the future, AI could be helpful in the prevention of chronic diseases, providing new diagnostic, therapeutic, and follow-up strategies. |
| format | Article |
| id | doaj-art-7e09f2c05fec4f3b9cba9a877859b98d |
| institution | Kabale University |
| issn | 2227-9709 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj-art-7e09f2c05fec4f3b9cba9a877859b98d2024-12-27T14:30:38ZengMDPI AGInformatics2227-97092024-11-011148610.3390/informatics11040086The Use of Artificial Intelligence to Analyze the Exposome in the Development of Chronic Diseases: A Review of the Current LiteratureStefania Isola0Giuseppe Murdaca1Silvia Brunetto2Emanuela Zumbo3Alessandro Tonacci4Sebastiano Gangemi5Department of Clinical and Experimental Medicine, School and Operative Unit of Allergy and Clinical Immunology, University of Messina, 98125 Messina, ItalyDepartment of Internal Medicine, University of Genoa, 16132 Genoa, ItalyDepartment of Clinical and Experimental Medicine, School and Operative Unit of Allergy and Clinical Immunology, University of Messina, 98125 Messina, ItalyDepartment of Clinical and Experimental Medicine, School and Operative Unit of Allergy and Clinical Immunology, University of Messina, 98125 Messina, ItalyInstitute of Clinical Physiology, National Research Council of Italy (IFC-CNR), 56124 Pisa, ItalyDepartment of Clinical and Experimental Medicine, School and Operative Unit of Allergy and Clinical Immunology, University of Messina, 98125 Messina, ItalyThe “Exposome” is a concept that indicates the set of exposures to which a human is subjected during their lifetime. These factors influence the health state of individuals and can drive the development of Noncommunicable Diseases (NCDs). Artificial Intelligence (AI) allows one to analyze large amounts of data in a short time. As such, several authors have used AI to study the relationship between exposome and chronic diseases. Under such premises, this study reviews the use of AI in analyzing the exposome to understand its role in the development of chronic diseases, focusing on how AI can identify patterns in exposure-related data and support prevention strategies. To achieve this, we carried out a search on multiple databases, including PubMed, ScienceDirect, and SCOPUS, from 1 January 2019 to 31 May 2023, using the MeSH terms (exposome) and (‘Artificial Intelligence’ OR ‘Machine Learning’ OR ‘Deep Learning’) to identify relevant studies on this topic. After completing the identification, screening, and eligibility assessment, a total of 18 studies were included in this literature review. According to the search, most authors used supervised or unsupervised machine learning models to study multiple exposure factors’ role in the risk of developing cardiovascular, metabolic, and chronic respiratory diseases. In some more recent studies, authors also used deep learning. Furthermore, the exposome analysis is useful to study the risk of developing neuropsychiatric disorders or evaluating pregnancy outcomes and child growth. Understanding the role of the exposome is pivotal to overcome the classic concept of a single exposure/disease. The application of AI allows one to analyze multiple environmental risks and their combined effects on health conditions. In the future, AI could be helpful in the prevention of chronic diseases, providing new diagnostic, therapeutic, and follow-up strategies.https://www.mdpi.com/2227-9709/11/4/86artificial intelligencechronic diseasedeep learningexposomemachine learning |
| spellingShingle | Stefania Isola Giuseppe Murdaca Silvia Brunetto Emanuela Zumbo Alessandro Tonacci Sebastiano Gangemi The Use of Artificial Intelligence to Analyze the Exposome in the Development of Chronic Diseases: A Review of the Current Literature Informatics artificial intelligence chronic disease deep learning exposome machine learning |
| title | The Use of Artificial Intelligence to Analyze the Exposome in the Development of Chronic Diseases: A Review of the Current Literature |
| title_full | The Use of Artificial Intelligence to Analyze the Exposome in the Development of Chronic Diseases: A Review of the Current Literature |
| title_fullStr | The Use of Artificial Intelligence to Analyze the Exposome in the Development of Chronic Diseases: A Review of the Current Literature |
| title_full_unstemmed | The Use of Artificial Intelligence to Analyze the Exposome in the Development of Chronic Diseases: A Review of the Current Literature |
| title_short | The Use of Artificial Intelligence to Analyze the Exposome in the Development of Chronic Diseases: A Review of the Current Literature |
| title_sort | use of artificial intelligence to analyze the exposome in the development of chronic diseases a review of the current literature |
| topic | artificial intelligence chronic disease deep learning exposome machine learning |
| url | https://www.mdpi.com/2227-9709/11/4/86 |
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