Artificial intelligence (AI) offers promising solutions for many steps of the cardiac imaging workflow, from patient and test selection through image acquisition, reconstruction, and interpretation, extending to prognostication and reporting. Despite the development of many cardiac imaging AI algorithms, AI tools are at various stages of development and face challenges for clinical implementation.

This collaborative multisociety effort explores artificial intelligence applications in cardiac CT and MRI, evaluating their readiness level from research to clinical deployment while addressing ethical, legal, and environmental sustainability considerations.

  • Various artificial intelligence (AI) applications to support cardiac CT and MRI workflows at different stages are available or under development at different maturity levels.
  • A nine-level technology readiness scale summarizes the maturity level of AI tools from preliminary research and development to clinical deployment.
  • Most AI tools for cardiac CT and MRI are predominantly in development stages, indicating a long journey ahead before realizing their full impact on clinical care.
  • Widespread AI clinical deployment should be done in parallel with efforts to address ethical concerns, improve cost-effectiveness, and promote sustainability.

Publish Date

Jan 29, 2025

Topic

  • Artificial Intelligence
  • Cardiology
  • Clinical Workflow & Productivity
  • Radiology
  • Research

Resource Type

  • Publications

Audience Type

  • Clinician
  • Developer
  • Imaging IT
  • Researcher/Scientist
  • Student Member in Training (SMIT)
  • Vendor

Resource

Teaching AI for Radiology Applications: A Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM

Oct 1, 2025

In a groundbreaking collaboration led by the SIIM Machine Learning Education Subcommittee, the American Association of Physicists in Medicine (AAPM), Radiological Society of North America (RSNA), and Society for Imaging Informatics in Medicine (SIIM) are proud to announce the simultaneous co-publication of “Teaching AI for Radiology Applications: A Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIM” in these three flagship journals: Medical Physics, Radiology: Artificial Intelligence, and the Journal of Imaging Informatics in Medicine (JIIM).

The society-endorsed syllabus outlines critical competencies for four key groups: clinical users, technology purchasers, clinical collaborators, and AI developers. Designed as a flexible framework, it allows institutions to adapt content while ensuring consistent instruction on AI fundamentals, clinical integration, regulatory requirements, and ethical considerations.

Resource

Multi-Agent AI and Ethics in Radiology: Navigating the Trust Crisis in Advanced Medical Systems
(Part 1)

Sep 9, 2025

Part 1: The Medical AI Background: From Simple Tools to Complex Systems Picture this: you’re a radiologist examining a chest…

Resource

Multi-Agent AI and Ethics in Radiology: Navigating the Trust Crisis in Advanced Medical Systems
(Part 2)

Sep 9, 2025

Part 2: Framework, Regulation & Future Directions Additions Ethical Considerations in Medical AI Deployment The deployment of multi-agent AI systems…