Start with the fundamentals. Learn key AI concepts through these five videos. You’ll hear how AI is developed and how AI tools can be applied to imaging data and improve radiology workflow. We’ll explore the areas of AI with the most tools available, look at bias and fairness in AI models, and explain what radiologists should look for in evaluating an AI algorithm.
What AI Is (and Is Not): 10 Thoughts
By: Katherine Andriole, PhD
- To describe at a high level what AI is and what it is not.
- The processes and infrastructure required for AI.
- The importance of data cohort design to AI.
- Examples of AI applied to imaging data and to radiology workflow.
Workflow-Based AI: Beyond Image Interpretation
By: Paras Lakhani, MD
What workflow-based AI includes.
Examples of workflow-based AI tools.
How workflow-based AI can result in performance gains for radiology practices.
Applications of Pixel-Based AI
By: Judy Wawira Gichoya, MBchB, MS
The current state of pixel-based AI systems.
The huge potential for pixel based AI products.
Bias and Fairness in AI Models
By: Monica J. Wood, MD
How biases make their way into AI algorithms.
Examples of bias affecting the training data.
How to engage in meaningful conversations on bias.
Brittleness of AI Models
By: Woojin Kim, MD
The limitations of AI and what can be done to mitigate them.
How validation and training issues contribute to brittleness.
What radiologists should look for in evaluating an AI algorithm.