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The number of data points per patient considered at the point-of-care in precision cancer medicine continues to increase, and it is accompanied by a growing challenge of translating these observations into clinical insights. This is a time-intensive and laborious process for oncology professionals and molecular tumour boards. As large clinicogenomic datasets and data-sharing protocols mature alongside machine learning methods, molecular diagnostic workflows have an opportunity to integrate these tools. This integration can help extract more information from next-generation sequencing data, enhance cancer variant interpretation, streamline case review and generate therapeutic hypotheses for biomarker-negative patients at the point-of-care. Although machine learning holds promise for precision oncology, responsible implementation and model evaluation remain essential for clinical adoption.