Artificial intelligence in clinical and genomic diagnostics
By Raquel Dias, Ali Torkamani. Genome Medicine, 2019
357 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Cancer Research, Genetics
View Paper: https://www.semanticscholar.org/paper/48f8ad3745c03879586381b59452b501e55a98b6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
Citations in the corpus
- Deep learning in cancer diagnosis, prognosis and treatment selection (2021, methodology): “Moreover, the use of MLPs, RNNs and CNNs to assist clinicians and researchers has been proposed across multiple oncology areas, including radiotherapy [9], digital histopathology [10, 11] and clinical and genomic diagnostics [7].”
- The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. (2021, background): “25 Taking this further, there is increasing research aiming to integrate genomics information with epigenetics, transcrip-tomics and proteomics to increase the understanding of biological networks in health and disease.”