Cannibalise, commoditise and consolidate – using AI in digital pathology

Signify Research casts an eye into the future of AI in clinical digital pathology following the publication of its most recent market analysis. To have the greatest clinical impact AI vendors must evolve to meet the demands of tomorrow’s customers. But, asks Imogen Fitt, what will this evolution look like?

Artificial intelligence (AI) is the subject of much debate across healthcare. Optimists laud the benefits it will bring for patients and professionals, whilst sceptics cite previous failures and a lack of technology maturity. However, it cannot be denied that standard pathology practice needs refinement.

Worldwide, pathology workforces are dwindling as case volumes are increasing, creating an ever-increasing strain which simply is not sustainable. One of the main proposed advantages in converting pathology departments to a digital workflow is the ability to apply image analysis and analyse data in a quantitative format; seldom are other options on offer, pushing many institutions to trial software to help build a base of evidence to support widespread deployment of such AI.

The definition of AI ‘Computer software able to execute problem-solving and decision-making capabilities normally requiring human intelligence’ is especially broad, and grouping all algorithms with sweeping statements on viability for real-world use isn’t helpful for prospective adopters. 

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