In traditional pathology detection, the speed of diagnosis is limited due to the complex staining process and single observation form. The staining process is essentially associating color information with morphological features, and the effect is equivalent to that of biomedical images of modern digital technology. Sense segmentation, which allows researchers to greatly reduce the steps of biomedical imaging processing samples through computational post-processing, and achieve imaging results consistent with the gold standard of traditional medical staining. In recent years, the development of artificial intelligence deep learning has contributed to the effective combination of computer-aided analysis and clinical medicine, and artificial intelligence color transfer technology has gradually shown high development potential in biomedical imaging analysis. This paper will review the technical principles of deep learning color transfer, enumerate some applications of such technologies in the field of biomedical imaging, and look forward to the research status and possible development trends of artificial intelligence color transfer in the field of biomedical imaging.