Biomedical deep learning (DL) often relies heavily on generating reliable labels for large-scale data and highly technical requirements for model training. To efficiently develop DL models, we established an integrated platform to introduce automation to both annotation and model training-the primary process of DL model development. Based on this platform, we quantitively validated and compared the annotation strategy and AI model development with the pure manual annotation method performed on medical image datasets from multiple disciplines.

Inclusion Criteria:

• have medical imaging record (including ophthalmology, pathology, radiography, blood cells, and endoscopy)

Exclusion Criteria:

• unqualified medical imaging

medical imaging for multiple disciplines including ophthalmology, pathology, radiography, blood cells, and endoscopy