An AI Factory for Medical Imaging

The advancement of medical AI development and deployment is making a significant impact on healthcare, helping researchers perform accurate early detection, medical classification, and advanced 3D automated segmentation. When these AI models are taken to the clinical environment as AI applications, they help clinicians streamline imaging workflows, uncover hidden insights, improve productivity, and connect multimodal patient information for deeper patient understanding. However, harnessing medical imaging data in ways that can scale AI development and deployment in the field faces several challenges:

> Limited availability of annotated, high-quality medical imaging data for AI training and validation

> Lack of reproducible tools for training workflows that can quickly develop generalizable AI models for clinical evaluation

> High computational demands for training deep learning models on large medical imaging datasets

> Difficulty in standardizing complex medical imaging analysis and the need for specialized expertise in AI and healthcare-specific domains

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