Narjice Chafai, Luigi Bonizzi, Sara Botti, Bouabid Badaoui
{"title":"Emerging applications of machine learning in genomic medicine and healthcare.","authors":"Narjice Chafai, Luigi Bonizzi, Sara Botti, Bouabid Badaoui","doi":"10.1080/10408363.2023.2259466","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":10760,"journal":{"name":"Critical reviews in clinical laboratory sciences","volume":" ","pages":"140-163"},"PeriodicalIF":6.6000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Critical reviews in clinical laboratory sciences","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/10408363.2023.2259466","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/10/10 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Critical Reviews in Clinical Laboratory Sciences publishes comprehensive and high quality review articles in all areas of clinical laboratory science, including clinical biochemistry, hematology, microbiology, pathology, transfusion medicine, genetics, immunology and molecular diagnostics. The reviews critically evaluate the status of current issues in the selected areas, with a focus on clinical laboratory diagnostics and latest advances. The adjective “critical” implies a balanced synthesis of results and conclusions that are frequently contradictory and controversial.