Bringing AI to Practice

July 6 – 7, 2019 | 5:00pm – 8:00pm | Washington D.C.
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Implementing AI can be challenging. Discover the steps to be successful. You’ll hear what to consider when incorporating AI into practice for the first time, including important processes to put into place. We’ll explain how to work with vendors, share issues with standardization, and dig into evaluating models for use in clinical practice.  Valuable ACR® informatics tools and the ACR Imaging 3.0 initiative are included.

Challenges in Executing an AI Project

By: Ali Ardestani MD, MSc, CIIP

There are three major workflow steps for any AI project: data capture, data processing and AI computing. This video reviews each step of the workflow, including potential challenges, and explains how the ACR AI-LAB™ can be used within the process.

Learning Objectives:
  • Workflow steps in an AI project.
  • Several methods for data capture.
  • How to generate and import ground truth in the ACR AI-LAB.

You've Purchased an AI Model — Now What?

By: Christoph Wald, MD, PhD, MBA, FACR

After you’ve purchased an AI model, there are challenges you might face when incorporating AI into your practice. This video explains how AI results can be incorporated into your existing workflow, shares local factors that can impact AI performance and the importance of providing feedback to vendors. 

Learning Objectives:
  • The major considerations related to AI development in clinical practice.
  • How to incorporate AI results into the routine workflow in a practical and meaningful way.
  • The importance of evaluating AI model performance and providing feedback to vendors.

Standardizing Deployment of AI Algorithms

By: Neil Tenenholtz, PhD, and Brian J. Bialecki, CIIP, CDIP, CAHIMS

At present, there are no standards for the deployment and execution of AI models in a clinical setting. Many model developers create custom platforms to host their models and present results, which often require additional IT personnel, to create more complex workflows with limited interoperability, and to increase the overall cost of ownership. This video reviews the barriers to entry created by the lack of standardization in AI deployment and how the ACR DSI workgroup is addressing them.

Learning Objectives:
  • Specific challenges created by the lack of standardization in AI deployment and how users are forced to address them.
  • How the ACR DSI is leading efforts to standardize AI deployment.

Effective Validation of AI Models Prior to Clinical Use

By: Bibb Allen Jr., MD, FACR

Prior to using an AI algorithm in clinical practice, radiologists must ensure it is relevant, safe and effective. FDA-cleared algorithms aren't guaranteed to function well at your practice and can be “brittle,” meaning they may fail to adapt to the conditions outside of the training environment. This video outlines the importance AI validation, the ACR approach to ensuring AI is safe and effective, and how ACR Connect is facilitating distributed validation.

Learning Objectives:
  • The challenges developers face in ensuring an algorithm will work in all clinical settings.
  • How to determine if a specific model will work at your practice.
  • The process for centralized vs. distributed algorithm validation.

Evaluating AI for Use in Your Clinical Practice

By: Bibb Allen Jr., MD, FACR

When evaluating an AI algorithm for clinical practice, radiologists must consider factors beyond FDA clearance. The FDA clearance process focuses on safety and efficacy, but does not ensure generalizability to all practices. Local assessment is essential to ensure a model will work in your practice. This video reviews the FDA clearance process, the importance of evaluating AI before implementation, and how the ACR AI-LAB can be used to evaluate AI using your own data.

Learning Objectives:
  • What the FDA clearance process of AI algorithms involves.
  • Specific challenges the FDA faces in the AI clearance process.
  • How to evaluate AI using your own patient data.

The Informatics Value Proposition for Radiologists

By: Bibb Allen Jr., MD, FACR

The Imaging 3.0 initiative promotes radiologists’ value to the healthcare system beyond imaging interpretation. Informatics resources including clinical decision support, structured reporting, image sharing, registries and AI have been key to the Imaging 3.0 program. This video gives an overview of the Imaging 3.0 initiative and why AI could become radiology’s next value proposition.

Learning Objectives:
  • Details of the ACR Imaging 3.0 initiative.
  • The value of the ACR informatics tools.
  • Why AI could become radiology’s next value proposition.