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The adage “a picture is worth a thousand words” isn’t just a reflection on traditional photographs. In fact, as much as 90% of healthcare data comes from imagery.1 Within each image hides key insights from disease type to patient demographics to dimensions, voxel size and repetition time. This data can influence diagnoses from healthcare providers, rare disease research, clinical trial design and much more.


While rich in insight, managing and analyzing medical imagery is incredibly complex. Images come in a variety of modalities, are produced by different departments such as radiology and pathology and are utilized differently across the healthcare organization – from providers to researchers to analysts.

In addition, image files are much larger than traditional data sources – making them difficult to share while taking up valuable storage. As a result, healthcare organizations spend far too much time and financial resources on imagery – from tasking highly-skilled professionals with tedious image analysis to managing the IT infrastructure needed to utilize these assets.


Most think of applying deep learning for image recognition, but these solutions provide much greater value when applied to analyzing metadata within medical imagery. Vyasa is changing the way healthcare organizations approach managing and from their content. Advanced deep learning image analytics built by Vyasa enable users to:

  • Unify and catalog image assets across the organization.
  • Enhance image detection and classification.
  • Make images easily searchable via simple annotation.

With a streamlined approach to image analytics with Vyasa, users can easily filter their image libraries to just their most relevant content, quickly identify cohorts for clinical trials, improve research accuracy for pathology and radiology reports and much more.

1. GE (2018) Beyond Imaging:the paradox of AI and medical imaging innovation ref1