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Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce
Artificial intelligence (AI) has been described as one of the extremely effective and promising scientific tools available to mankind. AI and its associated innovations are becoming more popular in industry and culture, and they are starting to show up in healthcare. Numerous facets of healthcare, a...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026833/ https://www.ncbi.nlm.nih.gov/pubmed/35455786 http://dx.doi.org/10.3390/healthcare10040608 |
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author | Wani, Shahid Ud Din Khan, Nisar Ahmad Thakur, Gaurav Gautam, Surya Prakash Ali, Mohammad Alam, Prawez Alshehri, Sultan Ghoneim, Mohammed M. Shakeel, Faiyaz |
author_facet | Wani, Shahid Ud Din Khan, Nisar Ahmad Thakur, Gaurav Gautam, Surya Prakash Ali, Mohammad Alam, Prawez Alshehri, Sultan Ghoneim, Mohammed M. Shakeel, Faiyaz |
author_sort | Wani, Shahid Ud Din |
collection | PubMed |
description | Artificial intelligence (AI) has been described as one of the extremely effective and promising scientific tools available to mankind. AI and its associated innovations are becoming more popular in industry and culture, and they are starting to show up in healthcare. Numerous facets of healthcare, as well as regulatory procedures within providers, payers, and pharmaceutical companies, may be transformed by these innovations. As a result, the purpose of this review is to identify the potential machine learning applications in the field of infectious diseases and the general healthcare system. The literature on this topic was extracted from various databases, such as Google, Google Scholar, Pubmed, Scopus, and Web of Science. The articles having important information were selected for this review. The most challenging task for AI in such healthcare sectors is to sustain its adoption in daily clinical practice, regardless of whether the programs are scalable enough to be useful. Based on the summarized data, it has been concluded that AI can assist healthcare staff in expanding their knowledge, allowing them to spend more time providing direct patient care and reducing weariness. Overall, we might conclude that the future of “conventional medicine” is closer than we realize, with patients seeing a computer first and subsequently a doctor. |
format | Online Article Text |
id | pubmed-9026833 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90268332022-04-23 Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce Wani, Shahid Ud Din Khan, Nisar Ahmad Thakur, Gaurav Gautam, Surya Prakash Ali, Mohammad Alam, Prawez Alshehri, Sultan Ghoneim, Mohammed M. Shakeel, Faiyaz Healthcare (Basel) Review Artificial intelligence (AI) has been described as one of the extremely effective and promising scientific tools available to mankind. AI and its associated innovations are becoming more popular in industry and culture, and they are starting to show up in healthcare. Numerous facets of healthcare, as well as regulatory procedures within providers, payers, and pharmaceutical companies, may be transformed by these innovations. As a result, the purpose of this review is to identify the potential machine learning applications in the field of infectious diseases and the general healthcare system. The literature on this topic was extracted from various databases, such as Google, Google Scholar, Pubmed, Scopus, and Web of Science. The articles having important information were selected for this review. The most challenging task for AI in such healthcare sectors is to sustain its adoption in daily clinical practice, regardless of whether the programs are scalable enough to be useful. Based on the summarized data, it has been concluded that AI can assist healthcare staff in expanding their knowledge, allowing them to spend more time providing direct patient care and reducing weariness. Overall, we might conclude that the future of “conventional medicine” is closer than we realize, with patients seeing a computer first and subsequently a doctor. MDPI 2022-03-24 /pmc/articles/PMC9026833/ /pubmed/35455786 http://dx.doi.org/10.3390/healthcare10040608 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Wani, Shahid Ud Din Khan, Nisar Ahmad Thakur, Gaurav Gautam, Surya Prakash Ali, Mohammad Alam, Prawez Alshehri, Sultan Ghoneim, Mohammed M. Shakeel, Faiyaz Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce |
title | Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce |
title_full | Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce |
title_fullStr | Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce |
title_full_unstemmed | Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce |
title_short | Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce |
title_sort | utilization of artificial intelligence in disease prevention: diagnosis, treatment, and implications for the healthcare workforce |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026833/ https://www.ncbi.nlm.nih.gov/pubmed/35455786 http://dx.doi.org/10.3390/healthcare10040608 |
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