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Artificial Intelligence in Imaging 2020

Artificial Intelligence in Imaging 2020
Shifting Mindshare

Authored by: Monique Rasband and Lauren Barton March 5, 2020 | Read Time: 7  minutes

Many provider organizations are exploring the deployment and adoption of artificial intelligence (AI) in imaging, and market competition has begun to increase among vendors who offer this technology. Based on feedback from 81 organizations with advanced imaging strategies, this report explores what healthcare organizations have planned for AI in imaging and their perceptions of which vendors are best positioned to help (responses could apply to modalities or software). It also identifies best practices related to AI deployment and adoption as reported by some of the healthcare industry’s most successful AI users. In the near future, KLAS plans to publish in-depth case studies from customers of some of the most frequently mentioned vendors to explore what is currently being achieved with AI in imaging.

Key Findings

  1. Defining AI in Healthcare
  2. State of the Market: How Have Provider Organizations’ Plans Changed since 2018?
  3. AI in Imaging Has Accelerated
  4. Deployment Is Closer Than Ever
  5. Depth of Adoption Expected to Increase
  6. Looking Ahead to a One-Stop Shop
  7. IBM Watson Health Leads in Mentions; Considerations of Other Vendors Increase
  8. Zebra Medical Vision Inspires Confidence While Gaining Visibility; GE Healthcare and Nuance Also Grow in Mindshare
  9. Other Mentioned Vendors
  10. Provider Perspectives on Top-of-Mind Vendors
  11. How Healthcare Organizations and Technology Vendors Can Drive Success with AI
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This material is copyrighted. Any organization gaining unauthorized access to this report will be liable to compensate KLAS for the full retail price. Please see the KLAS DATA USE POLICY for information regarding use of this report. © 2020 KLAS Research, LLC. All Rights Reserved. NOTE: Performance scores may change significantly when including newly interviewed provider organizations, especially when added to a smaller sample size like in emerging markets with a small number of live clients. The findings presented are not meant to be conclusive data for an entire client base.