Benchmarking zero-shot large language models against supervised, tailor-made predictors on cancer-type, somatic-vs-CHIP and variant-oncogenicity classification across 100,000+ samples from 19 institutions, and combining them into LLM–specialist ensembles that raise cross-institution accuracy by up to 55%.
