Chafai Narjice, Bonizzi Luigi, Botti Sara, Badaoui Bouabid
Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco.
Department of Biomedical, Surgical and Dental Science, University of Milan, Milan, Italy.
Crit Rev Clin Lab Sci. 2024 Mar;61(2):140-163. doi: 10.1080/10408363.2023.2259466. Epub 2023 Oct 10.
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.
近年来，人工智能技术的整合推动了临床和基因组医学的发展。计算能力的显著提升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，从而推动了智能诊断工具的进步。人工智能（AI）模型已被应用于精准医学领域，用于整合患者的临床数据和基因组信息。这种整合有助于确定定制化的治疗建议，最终改善患者的治疗效果。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可用性有限、数据集的多样性、伦理问题以及对人工智能模型结果的解释不明确等。在本综述中，我们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情况。最后，我们研究了医疗行业中人工智能实施方面的一些限制因素。
