Emerging applications of machine learning in genomic medicine and healthcare.
The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data a...
| Publicado en: | Critical Reviews in Clinical Laboratory Sciences Vol. 61; no. 2; pp. 140 - 164 |
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| Autores principales: | , , , |
| Formato: | algorithm equations & formulas pictorial review tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Mar2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175637462&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175637462 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10408363 1AV jtl: Critical Reviews in Clinical Laboratory Sciences issn: 10408363 maglogo: Y pubinfo: dt: Mar2024 vid: 61 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 175637462 172891684 175637462 175637462 10.1080/10408363.2023.2259466 175637462 ppf: 140 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Emerging applications of machine learning in genomic medicine and healthcare. aug: au: Chafai, Narjice Bonizzi, Luigi Botti, Sara Badaoui, Bouabid affil: Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco sug: subj: Algorithms Utilization Machine Learning Utilization Genomic Medicine Health Care Industry Artificial Intelligence Deep Learning Drug Discovery Models, Theoretical Logistic Regression Workflow ab: The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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