arXiv:2606.26157v1 Announce Type: cross Abstract: The rapid growth of digital pathology has created an urgent need for efficient indexing and retrieval of whole slide images (WSIs). This need is intensified by emerging generative AI workflows, particularly retrieval-augmented generation (RAG), which require dependable similarity search to support high-stakes clinical decision-making. Yet the substantial cost of high-performance storage limits the scalability and accessibility of WSI indexing for many healthcare institutions. Consequently, methods that can reduce storage demands while preservin
The proliferation of digital pathology and the demands of generative AI workflows, especially RAG, are creating acute storage and retrieval challenges that require novel solutions for scalability.
Efficient indexing and retrieval of whole slide images are critical for scaling AI in healthcare, directly impacting clinical decision-making and operational costs for institutions.
This research will enable healthcare institutions to manage vast amounts of pathology data more affordably and efficiently, accelerating the adoption of AI in diagnostics.
- · Healthcare institutions
- · AI in pathology developers
- · Digital pathology vendors
- · Patients
- · High-cost data storage providers
- · Inefficient AI data management practices
Reduced storage costs and improved accessibility for large-scale whole slide image datasets.
Faster development and deployment of robust AI diagnostic tools in pathology.
Enhanced diagnostic accuracy and throughput in healthcare, leading to earlier disease detection and personalized treatment plans.
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Read at arXiv cs.AI