Clinical Validation and Continuous Monitoring of Artificial Intelligence in Medical Imaging: Lessons from Platform-Based Deployment.
- 1CARPLDelhiIndia.
Abstract
The deployment of artificial intelligence (AI) in clinical radiology has expanded rapidly worldwide, yet a persistent " deployment gap " remains between research performance and real-world clinical utility. The deployment gap refers to the erosion of AI performance when a model trained and validated in controlled environments is exposed to heterogeneous imaging hardware, acquisition protocols, patient demographics, and reporting practices of routine clinical work. A high-performing algorithm is a necessary but insufficient condition for a successful clinical tool. This narrative review presents a five-stage framework for the full AI clinical lifecycle: pre-deployment evaluation with data acquisition and ground truth creation, local clinical validation in shadow mode, workflow orchestration and deployment, continuous post-deployment monitoring, and error analysis with closed-loop improvement. It also considers application of this framework in the specific context of regulatory environments and data governance obligations. Central to our approach is the insight that continuous monitoring requires only three data streams: the AI output, the original study with its DICOM metadata, and an independently sourced ground truth. When these three signals are reconciled in near real-time, institutions can detect performance drift quickly, identify the hardware or protocol correlates of failure, and trigger targeted corrective action-such as filtering out-of-specification inputs-while any model update remains the vendor's responsibility under regulated change control, without modifying the locked device on site. We illustrate the framework using a composite case study of pneumothorax detection drift following a hardware upgrade. We conclude with a practical implementation checklist calibrated to the infrastructure realities of Indian and other resource-variable health systems worldwide, and a call for the Indian Radiological and Imaging Association to develop minimum standards for AI post-market surveillance.
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BibTeX
@article{mahajan2026clinical,
title = {{Clinical Validation and Continuous Monitoring of Artificial Intelligence in Medical Imaging: Lessons from Platform-Based Deployment.}},
author = {Vidur Mahajan and Himanshu Makkar and Abhishek Gupta and Aakanksha Doda and Devanshi Thakkar},
journal = {The Indian journal of radiology \& imaging},
year = {2026},
month = sep,
doi = {10.1055/s-0046-1827841},
url = {https://europepmc.org/article/MED/42921629},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Clinical Validation and Continuous Monitoring of Artificial Intelligence in Medical Imaging: Lessons from Platform-Based Deployment.
AU - Vidur Mahajan
AU - Himanshu Makkar
AU - Abhishek Gupta
AU - Aakanksha Doda
AU - Devanshi Thakkar
PY - 2026
DA - 2026/09/11
JO - The Indian journal of radiology & imaging
DO - 10.1055/s-0046-1827841
UR - https://europepmc.org/article/MED/42921629
AB - The deployment of artificial intelligence (AI) in clinical radiology has expanded rapidly worldwide, yet a persistent " deployment gap " remains between research performance and real-world clinical utility. The deployment gap refers to the erosion of AI performance when a model trained and validated in controlled environments is exposed to heterogeneous imaging hardware, acquisition protocols, patient demographics, and reporting practices of routine clinical work. A high-performing algorithm is a necessary but insufficient condition for a successful clinical tool. This narrative review presents a five-stage framework for the full AI clinical lifecycle: pre-deployment evaluation with data acquisition and ground truth creation, local clinical validation in shadow mode, workflow orchestration and deployment, continuous post-deployment monitoring, and error analysis with closed-loop improvement. It also considers application of this framework in the specific context of regulatory environments and data governance obligations. Central to our approach is the insight that continuous monitoring requires only three data streams: the AI output, the original study with its DICOM metadata, and an independently sourced ground truth. When these three signals are reconciled in near real-time, institutions can detect performance drift quickly, identify the hardware or protocol correlates of failure, and trigger targeted corrective action-such as filtering out-of-specification inputs-while any model update remains the vendor's responsibility under regulated change control, without modifying the locked device on site. We illustrate the framework using a composite case study of pneumothorax detection drift following a hardware upgrade. We conclude with a practical implementation checklist calibrated to the infrastructure realities of Indian and other resource-variable health systems worldwide, and a call for the Indian Radiological and Imaging Association to develop minimum standards for AI post-market surveillance.
SN - 0971-3026
SN - 1998-3808
ER -