The Scripps Translational Science Institute, The Scripps Research Institute, 3344 North Torrey Pines Court Suite 300, La Jolla, CA, 92037, USA.
Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 3344 North Torrey Pines Court Suite 300, La Jolla, CA, 92037, USA.
Genome Med. 2019 Nov 19;11(1):70. doi: 10.1186/s13073-019-0689-8.
Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.
人工智能（AI）是计算机系统的发展，这些系统能够执行通常需要人类智能的任务。人工智能软件和硬件的进步，特别是深度学习算法和为其训练提供支持的图形处理单元（GPU），使得人们对医学人工智能应用的兴趣最近迅速增加。在临床诊断中，基于人工智能的计算机视觉方法有望彻底改变基于图像的诊断，而其他人工智能亚型也开始在各种诊断模式中显示出类似的前景。在某些领域，如临床基因组学，一种称为深度学习的特定类型的人工智能算法被用于处理大型和复杂的基因组数据集。在这篇综述中，我们首先总结了人工智能系统适合解决的主要问题类别，并描述了受益于这些解决方案的临床诊断任务。接下来，我们专注于临床基因组学中特定任务的新兴方法，包括变异调用、基因组注释和变异分类，以及表型到基因型的对应关系。最后，我们讨论了人工智能在个体化医学应用中的未来潜力，特别是在常见复杂疾病的风险预测方面，以及在成功部署人工智能应用于医学应用，特别是利用人类遗传学和基因组学数据的应用时，必须仔细解决的挑战、限制和偏见。
