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
Related News
•EurekAlert
AI Framework Accelerates Aortic Aneurysm Risk Prediction from Imaging
Researchers developed BioPINN-LM, combining physics-informed neural networks and multimodal large language models to deliver fast, interpretable risk assessments for ascending thoracic aortic aneurysms.
•EurekAlert
Study Finds Patient Voices Missing in Generative AI Design for Oncology
A Flinders University-led review found patients and carers are rarely involved in shaping generative AI tools used in oncology.
•EurekAlert
Scripps Researchers Develop AI Foundation Model for ECG-Based Heart Disease Prediction
A new AI foundation model, ECG-CLIP, improves detection and prediction of multiple heart diseases using large-scale ECG and clinician note data.