Artificial Intelligence in Rad-AI-tion Therapy: Bridging the Gap between Theory and Practice

Facilitator(s): Meegan Shepherd, Kenton Thompson, Scott Jones, Julie O'Shaughnessey, Alexandra Grimshaw

Synopsis: Artificial intelligence (AI) tools are now embedded in most radiation therapy departments, but governance, evaluation and accountability have not kept pace. This workshop moves past the introductory question of what AI can do and into the harder one of who is responsible when it underperforms. Participants will work through local commissioning and acceptance testing, learn to interrogate vendor performance claims against their own data, explore failure modes and automation bias, and build a departmental governance and competency plan they can take home. Case-based and highly interactive, with a strong focus on the Radiation Therapists professional accountability.

Learning outcomes: 

  • Evaluate AI tool performance against local patient data rather than relying on vendor-reported metrics
  • Identify common failure modes in AI-assisted workflows, including automation bias and silent degradation
  • Apply a governance framework to AI implementation, covering commissioning, monitoring, escalation and documentation
  • Map competency and credentialing requirements for RTs working in AI-enabled workflows

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