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  1. Home
  3. Developing Algorithm-based Recommendations In The Acr: Defining A New Process.
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  3. Developing Algorithm-based Recommendations In The Acr: Defining A New Process.

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Developing Algorithm-Based Recommendations in the ACR: Defining a New Process.

Stella K Kang1, Jenny Hoang2, Tarik Alkasab3

  • 1Vice Chair of Research, Professor, Columbia University Irving Medical Center, New York, New York.

Journal of the American College of Radiology : JACR
|November 10, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A new, formal process for developing algorithm-based imaging recommendations by the American College of Radiology (ACR) has been established. This evidence-driven approach enhances scalability and generalizability for future clinical guidelines.

Keywords:
Evidence synthesisincidental findingprocessqualityrecommendations

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Area of Science:

  • Radiology
  • Medical Informatics
  • Clinical Guidelines

Background:

  • The American College of Radiology (ACR) previously used informal consensus for algorithm-based recommendations, limiting scalability and formal guideline designation.
  • The lack of a formal, evidence-driven process hindered incidental findings and other algorithms from meeting the National Academy of Medicine's Trustworthy Guidelines criteria.
  • Limited referrer and policymaker adoption was observed due to the absence of a standardized, evidence-based development process.

Purpose of the Study:

  • To introduce a new, formal, evidence-driven process for developing algorithm-based imaging recommendations within the ACR.
  • To overcome the limitations of the previous informal consensus method, including scalability and generalizability.
  • To enable ACR algorithms to meet criteria for designation as clinical guidelines.

Main Methods:

  • Formation of a multi-stakeholder committee with ACR approval, including representatives from key ACR committees, commissions, and diverse practice settings.
  • Development of a formal, evidence-driven methodology for creating algorithm-based imaging recommendations.
  • Ensuring the process is generalizable for adoption by various ACR committees.

Main Results:

  • A new, formal, evidence-driven process for algorithm-based imaging recommendations has been successfully developed and documented.
  • The new process addresses the limitations of informal consensus, enhancing scalability and generalizability.
  • This formalized approach is designed to facilitate compliance with the National Academy of Medicine's Trustworthy Guidelines criteria.

Conclusions:

  • The newly developed evidence-driven process provides a robust framework for creating ACR algorithm-based imaging recommendations.
  • This formalized process is expected to improve the quality, adoption, and guideline designation of ACR recommendations.
  • The methodology is generalizable and can be adopted by other ACR committees for their evidence-based recommendation development.