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Updated: Jul 3, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

State of the AI: Post-Deployment Monitoring of Radiology-Focused Internally Developed AI.

Cole J Cook¹, Jason R Klug¹, Blaize W Kandler¹

¹Department of Radiology, Mayo Clinic, Rochester, MN.

Mayo Clinic Proceedings. Digital Health

|March 9, 2026

Summary

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Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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Mayo Clinic developed a framework to deploy 17 medical image artificial intelligence (AI) algorithms into clinical practice. Continuous monitoring ensures optimal performance and minimizes risks associated with AI deployment in radiology.

Area of Science:

Radiology
Artificial Intelligence
Clinical Translation

Background:

Numerous articles focus on medical image AI algorithm development, but clinical deployment is less discussed.
Bridging the gap between AI development and clinical practice is crucial for effective healthcare integration.

Purpose of the Study:

To describe the experience and methods used for the clinical translation and monitoring of 17 medical image AI algorithms.
To highlight the importance of continuous monitoring for successful AI deployment in radiology.

Main Methods:

Implementation of the Enterprise Radiology Framework for AI Software Technology.
Daily, weekly, and yearly monitoring of algorithm utilization, failure modes, data drift, and end-user feedback.
Utilizing automated alerts, dashboards, and investigations for proactive problem identification.

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Last Updated: Jul 3, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Main Results:

Successfully released 17 AI algorithms into clinical radiology practice.
Automated monitoring enabled early detection of issues, preventing clinical disruption.
Monitoring reinforced the value of interdisciplinary collaboration and training.

Conclusions:

Continuous monitoring is essential for the successful clinical translation of medical image AI.
Proactive monitoring minimizes risks and maximizes the benefits of AI in healthcare.
Community sharing of AI monitoring experiences can advance safe and effective AI implementation.