American Roentgen Ray Society Clinical Implementation of Artificial Intelligence 2025

September 15, 2025
This Online Course equips radiologists with the knowledge and practical skills that are needed to integrate AI tools into their practices. Leading experts review best practices for selecting AI tools and enhancing their value in order to: maximize return on investment, streamline clinical workflows, and implement effective methods to monitor the impact of AI systems on clinical practice.
Learning Outcomes and Modules
After completing this course, the learner should be able to:

Critically assess and select AI tools aligned with clinical needs.
Develop strategies to maximize return on investment in AI.
Gain insights into best practices for integrating AI tools into clinical practice.
Implement effective methods to monitor the impact of AI tools on clinical practice.

The American Roentgen Ray Society (ARRS) – Clinical Implementation of Artificial Intelligence 2025 program is best for radiologists, imaging scientists, and healthcare professionals who want to understand how AI can be practically integrated into daily radiology workflows. It emphasizes real‑world applications, evidence‑based evaluation, and strategies for safe, effective adoption of AI tools in clinical practice.
Who Should Attend

Radiologists across subspecialties seeking to incorporate AI into diagnostic imaging.
Residents & fellows in radiology learning how AI impacts training and practice.
Medical physicists & imaging scientists developing or validating AI algorithms.
Healthcare administrators & IT leaders overseeing AI implementation in hospital systems.
Oncologists, surgeons, and clinicians collaborating with radiology teams using AI‑enhanced imaging.

What You’ll Learn

AI in radiology workflows: triage, detection, segmentation, and reporting.
Clinical validation: evaluating accuracy, reproducibility, and limitations of AI tools.
Integration strategies: how to safely implement AI in PACS/RIS systems.
Ethical & regulatory considerations: patient safety, bias, and FDA/CE approvals.
Case‑based instruction: examples of AI improving efficiency and diagnostic precision.
Future directions: evolving AI applications in radiology and multidisciplinary care.

 
+ Topics:

Overview of Clinical Implementation Considerations and Challenges—Manisha Bahl
Selection of AI Tools—Mark Hammer
Best Practices for Successful Deployment of AI—Avishkar Sharma
Clinical Implementation Considerations: An International Perspective—Federica Vernuccio
Lessons Learned from Clinical Implementation—Jason Wiesner