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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

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Updated: Aug 21, 2025

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

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ACR's Connect and AI-LAB technical framework.

Laura Brink1, Laura P Coombs1, Deepak Kattil Veettil1

  • 1Department of Information Technology, American College of Radiology, Reston, Virginia, USA.

JAMIA Open
|November 16, 2022
PubMed
Summary
This summary is machine-generated.

The American College of Radiology developed ACR Connect, a free software suite democratizing artificial intelligence (AI) in healthcare. This platform facilitates AI model development and collaboration, enabling broader participation from non-technical users.

Keywords:
artificial intelligencedata sciencemachine learningradiologysoftware design

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Health Informatics

Background:

  • Artificial intelligence (AI) has significantly advanced medical imaging analysis but remains underutilized in clinical settings.
  • Successful AI development requires collaboration between clinicians, data scientists, and access to quality datasets.
  • Current AI adoption is hindered by a lack of accessible platforms for development and validation.

Purpose of the Study:

  • To develop a free, vendor-neutral software suite, ACR Connect, to democratize artificial intelligence (AI) for all individuals and institutions.
  • To create a collaborative AI ecosystem across clinical and technical domains.
  • To enable non-technical users to participate in AI education and model development.

Main Methods:

  • ACR Connect offers educational resources, tools for dataset annotation, model building, and evaluation.
  • It provides an interface for collaboration and federated learning without data transfer.
  • The AI-LAB application within ACR Connect allows local data investigation while maintaining security.

Main Results:

  • ACR Connect enables users to investigate AI models using local data securely via the AI-LAB application.
  • The platform facilitates non-technical users' participation in AI model evaluation and training within a collaborative network.
  • It supports external validation and collaborative, distributed training of AI models.

Conclusions:

  • ACR Connect provides a foundational platform for democratizing AI in healthcare.
  • The software empowers non-technical users to engage in AI development and education.
  • It fosters a collaborative AI ecosystem essential for advancing clinical applications.