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  3. Postdeployment Monitoring And Surveillance Methods, Guidelines, And Possibilities For Ai In Radiology.
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  3. Postdeployment Monitoring And Surveillance Methods, Guidelines, And Possibilities For Ai In Radiology.

Related Experiment Video

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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

Published on: July 11, 2025

Postdeployment Monitoring and Surveillance Methods, Guidelines, and Possibilities for AI in Radiology.

Vasantha Kumar Venugopal1,2, Suyash Anil Khubchandani3, Charlene Jin Yee Liew4

  • 1Imaging Division, Rajiv Gandhi Cancer Institute and Research Centre, Sir Chotu Ram Marg, Sector 5, Rohini, New Delhi, India 110085.

Radiographics : a Review Publication of the Radiological Society of North America, Inc
|June 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Postdeployment monitoring of AI in radiology is crucial for patient safety. A human-on-the-loop (HOTL) model offers a balanced approach to AI oversight, ensuring responsible scaling of these technologies.

Related Experiment Videos

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

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

Published on: July 11, 2025

Area of Science:

  • Radiology AI
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Radiology AI systems are transitioning from validation to routine clinical practice.
  • Postdeployment monitoring and postmarket surveillance are essential components of the total product life cycle (TPLC) for AI.
  • Human clinical oversight models for AI range from human-in-the-loop (HITL) to human-out-of-the-loop (HOOTL).

Purpose of the Study:

  • To define human clinical oversight models in AI radiology.
  • To propose the human-on-the-loop (HOTL) model as a pragmatic approach for high-stakes imaging.
  • To present a proactive monitoring system for AI in radiology.

Main Methods:

  • Defined a spectrum of human clinical oversight models (HITL, HIPL, HOTL, HOVL, HOOTL).
  • Proposed a monitoring system using temporal stability of inputs/outputs and predictive divergence.
  • Incorporated uncertainty quantification to prioritize reviews.

    • Main Results:

    • The HOTL model balances verification workload, autonomy, and risk for AI oversight.
    • The monitoring system utilizes data points not requiring immediate ground truth determination.
    • A threshold-based alerting system with tiered escalation and root cause analysis is described.

    Conclusions:

    • A proactive roadmap for postdeployment monitoring of radiology AI is presented.
    • The proposed system prioritizes patient safety while enabling responsible AI scaling.
    • This approach supports the continuous evaluation and improvement of AI in clinical practice.