Emerging applications of machine learning in genomic medicine and healthcare.

IF 6.6 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY Critical reviews in clinical laboratory sciences Pub Date : 2024-03-01 Epub Date: 2023-10-10 DOI:10.1080/10408363.2023.2259466
Narjice Chafai, Luigi Bonizzi, Sara Botti, Bouabid Badaoui
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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.

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机器学习在基因组医学和医疗保健中的新兴应用。
近年来,人工智能技术的融合推动了临床和基因组医学的进步。计算能力的显著提高促进了人工智能模型从大量医学数据和图像中分析和提取特征的能力,从而有助于智能诊断工具的进步。人工智能(AI)模型已被用于个性化医学领域,以整合患者的临床数据和基因组信息。这种集成可以确定定制的治疗建议,最终提高患者的治疗效果。尽管取得了显著进展,但人工智能在医学领域的应用仍受到各种障碍的阻碍,如临床和基因组数据的有限可用性、数据集的多样性、伦理影响以及对人工智能模型结果的不确定解释。在这篇综述中,对临床和基因组医学领域中使用的多种机器学习算法进行了全面评估。此外,我们还概述了人工智能在临床医学、药物发现和基因组医学领域的应用。最后,研究了在医疗保健行业中实施人工智能的一些限制因素。
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来源期刊
CiteScore
20.00
自引率
0.00%
发文量
25
审稿时长
>12 weeks
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.
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