Rohan Paul@rohanpaul_ai · X新发表于《自然-通讯》的研究开发了一种多模态AI测试，通过常规H&E染色切片和临床数据来评估侵袭性乳腺癌亚型的复发风险，正确率约71%。系统使用预训练图像引擎Kestrel，该模型从4亿个病理组织图像块中学习，无需人工标注即可对肿瘤形态进行评分，在传统指标（肿瘤大小、淋巴结、受体、分级、基因检测）基础上补充了常规组织结构中隐藏的复发风险视觉线索。
New nature-published research built a breast cancer AI test that uses routine microscope slides and clinical data to rank recurrence risk correctly about 71% of the time.
A foundation AI model learned tissue patterns from 400M patches， then helped turn routine slides into recurrence-risk evidence.
Breast cancer care already uses tumor size， nodes， receptors， grade， and sometimes gene assays.
Those signals guide therapy， but they still miss hidden recurrence risk inside ordinary tissue structure.
This system adds the missing visual layer by reading digitized pathology slides alongside routine clinical data.