AI Training: The Big Picture
Katrien Grünberg introduces Bigpicture, an ambitious project to raise the bar of AI-augmented pathology
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By
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Helen Bristow
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June 10, 2026
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Objective:
To create a comprehensive platform for AI-assisted pathology by pooling high-quality data sets from various stakeholders, emphasizing collaboration.
Approach:
Key Findings:
- Quality of AI applications is directly linked to the quality of training data.
- A diverse consortium is essential for pooling high-quality data.
- Both rare and routine pathology cases are important for AI model development.
- Compliance with privacy regulations is critical for data sharing.
Interpretation:
The Bigpicture initiative represents a collaborative effort to enhance AI in pathology through a structured approach to data sharing.
Limitations:
- The success of the platform relies on the willingness of institutions to contribute data.
- Data access is contingent on alignment with ethical frameworks and intended use.
Conclusion:
Bigpicture aims to create a robust ecosystem for AI in pathology, focusing on data quality and accessibility challenges.
