Mimicking human intelligence is the inspiration for AI algorithms, but AI applications in clinical genomics tend to target tasks that are impractical to perform using human intelligence and error prone when addressed with standard statistical approaches. Many of the techniques described above have been adapted to address the various steps involved in clinical genomic analysis—including variant calling, genome annotation, variant classification, and phenotype-to-genotype correspondence—and perhaps eventually they can also be applied for genotype-to-phenotype predictions. Here, we describe the major classes of problems that have been addressed by AI in clinical genomics.
