Using artificial intelligence (AI) to model clinical variant reporting for next generation sequencing (NGS) oncology assays

Abstract

Longitudinally acquired NGS assay data provide a strong basis for machine learning models for decision support to select variants for clinical oncology reports. The models provide a framework for consistent reporting practices and reducing inter-reviewer variability. To improve model transparency, individual variant predictions are able to be presented as part of reviewer workflows.

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