A new lifecycle framework outlines practical steps for sustainable digital pathology and AI program implementation in clinical labs.
Key Details
- 1Digital pathology (DP) is shifting from an adjunct to a primary diagnostic tool in U.S. labs.
- 2Persistent barriers include high costs, interoperability, workflow disruption, and regulatory requirements.
- 3The framework covers infrastructure, workflow redesign, compliance, cost management, interoperability, security, education, and governance.
- 4AI readiness assessment includes data quality, integration, validation, monitoring, governance, and workforce training.
- 5Emphasizes separation between device authorization and lab validation for optimal quality management.
Why It Matters
As digital pathology adoption accelerates, labs require robust frameworks to navigate the technical, clinical, regulatory, and AI readiness demands. This comprehensive approach positions institutions to achieve clinical benefits and to safely implement future imaging AI tools.
Source
EurekAlert
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