A Multisite, Report-Based, Centralized Infrastructure for Feedback and Monitoring of Radiology AI/ML Development and Clinical Deployment

• URL: http://arxiv.org/abs/2008.13781v1
• Date: Mon, 31 Aug 2020 17:59:04 GMT
• Title: A Multisite, Report-Based, Centralized Infrastructure for Feedback and Monitoring of Radiology AI/ML Development and Clinical Deployment
• Authors: Menashe Benjamin, Guy Engelhard, Alex Aisen, Yinon Aradi, Elad Benjamin
• Abstract summary: An interactive radiology reporting approach integrates image viewing, dictation, natural language processing (NLP) and creation of hyperlinks between image findings and the report. These images and labels can be captured and centralized in a cloud-based system. The method addresses proposed regulatory requirements for post-marketing surveillance and external data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An infrastructure for multisite, geographically-distributed creation and collection of diverse, high-quality, curated and labeled radiology image data is crucial for the successful automated development, deployment, monitoring and continuous improvement of Artificial Intelligence (AI)/Machine Learning (ML) solutions in the real world. An interactive radiology reporting approach that integrates image viewing, dictation, natural language processing (NLP) and creation of hyperlinks between image findings and the report, provides localized labels during routine interpretation. These images and labels can be captured and centralized in a cloud-based system. This method provides a practical and efficient mechanism with which to monitor algorithm performance. It also supplies feedback for iterative development and quality improvement of new and existing algorithmic models. Both feedback and monitoring are achieved without burdening the radiologist. The method addresses proposed regulatory requirements for post-marketing surveillance and external data. Comprehensive multi-site data collection assists in reducing bias. Resource requirements are greatly reduced compared to dedicated retrospective expert labeling.

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