  Medical image classification requires labeled, task-specific datasets which
are used to train deep learning networks de novo, or to fine-tune foundation
models. However, this process is computationally and technically demanding. In
language processing, in-context learning provides an alternative, where models
learn from within prompts, bypassing the need for parameter updates. Yet,
in-context learning remains underexplored in medical image analysis. Here, we
systematically evaluate the model Generative Pretrained Transformer 4 with
Vision capabilities (GPT-4V) on cancer image processing with in-context
learning on three cancer histopathology tasks of high importance:
Classification of tissue subtypes in colorectal cancer, colon polyp subtyping
and breast tumor detection in lymph node sections. Our results show that
in-context learning is sufficient to match or even outperform specialized
neural networks trained for particular tasks, while only requiring a minimal
number of samples. In summary, this study demonstrates that large vision
language models trained on non-domain specific data can be applied out-of-the
box to solve medical image-processing tasks in histopathology. This
democratizes access of generalist AI models to medical experts without
technical background especially for areas where annotated data is scarce.
