Advances in slide scanning, cloud storage, and AI are turning digital pathology images into data that can be analyzed at scale.
Researchers in the field say the implications of AI in digital pathology extend beyond image analysis.
Large language models can help clinicians navigate a research landscape that produces “hundreds of papers every single day” to inform ongoing cancer research.
Multimodal AI tools promise to unlock even more insights from digital pathology data by combining it with genomic, radiomic, and clinical data to build powerful new models of both common and rare cancers for diagnosis, drug development, and clinical trial enrollment.
As the field of applying AI to digital pathology progresses, it needs to build the groundwork for a wider range of potential applications that could address rare cancers and other areas without an abundance of data.
Foundation models like Atlas allow large-scale pre-training of data to develop numerical representations called embeddings that capture both the structural and contextual features of slides in the dataset.
The promise of AI in oncology isn't just better algorithms, it's broader access.
The maturation of computational pathology and its dissemination from large cancer centers like Moffitt to regional and rural health systems has the potential to provide levels of care typically only available at large research hospitals in community settings as well.
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