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Abstract Artificial intelligence has high potential for impact in healthcare applications, but its training and deployment are challenging due to diverse data, a complex spectrum of possible tasks and important privacy needs. High-performing foundation models that enable data-efficient fine-tuning for diverse downstream tasks can meaningfully accelerate development in this domain. We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3.
Reviewing sentinel lymph node biopsies for evidence of metastasis is an important feature of breast cancer staging, concurrently impacting clinical staging and treatment decisions.1 However, reviewing lymph nodes for the presence of tumor cells is a tedious, time-consuming, and potentially error-prone process.
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