AI in Clinical and Genomic Diagnostics: From Variant Calling to Phenotype-Genotype Mapping
This Genome Medicine review provides a comprehensive overview of AI applications across clinical diagnostics, with particular depth on genomic analysis tasks including variant calling, genome annotation, variant classification, and phenotype-to-genotype correspondence. The authors survey computer vision approaches poised to transform image-based diagnostics and discuss the emerging potential of AI for individualized risk prediction in complex diseases. They conclude with a candid assessment of biases and deployment challenges that must be addressed for safe clinical use.
The original study
Artificial intelligence in clinical and genomic diagnostics.
- Authors
- Dias R, Torkamani A
- Journal
- Genome medicine
- Type
- Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Review
- PMID
- 31744524
Original abstract
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.