The primary challenge in advancing biomedical imaging AI is not the development of more sophisticated models, but rather the accessibility and usability of the data itself. Imaging data is often siloed within hospital systems like PACS, making it difficult to extract, de-identify, and integrate with other critical health information such as EHR and omics data. This data fragmentation hinders the generalization of AI models across different vendors and clinical settings, and limits reproducibility in academic research. To overcome this, a robust data foundation is needed that centralizes, queries, and links diverse data sources, enabling the true potential of AI in healthcare. AI
IMPACT Highlights the critical need for better data infrastructure to unlock the full potential of AI in biomedical imaging.
RANK_REASON The article discusses challenges and potential solutions in the field of biomedical imaging AI, focusing on data infrastructure rather than a specific product release or research breakthrough.
- 3DSlicer
- Canon
- Databricks
- Douglas Moore
- Fastmri
- GE HealthCare
- MIMIC-CXR
- Monai
- nnU-Net
- Parastou Eslami
- picture archiving and communication system
- Siemens Healthineers
- The Cancer Imaging Archive
- UK Biobank
- United States Food and Drug Administration
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