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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Artificial Intelligence (AI) adoption in healthcare, particularly oncology, is accelerating.
  • Existing AI frameworks are generic, lacking specialized governance for oncology.
  • There is a critical need for robust AI governance models in cancer care.

Purpose of the Study:

  • To report on a Comprehensive Cancer Center's Responsible AI governance model for clinical, operational, and research applications.
  • To share the outcomes of the first year of AI governance committee activities.
  • To introduce novel tools and methodologies for AI governance in oncology.

Main Methods:

  • Established an AI Governance Committee to oversee AI implementation.
  • Registered and monitored 26 AI models, including large language models and ambient AI pilots.
  • Reviewed 33 nomograms and developed management tools: program model, information sheet, risk assessment, and lifecycle management.
  • Utilized case studies and an "Express Pass" methodology for select AI models.

Main Results:

  • Successfully implemented a Responsible AI governance model over one year.
  • Managed 26 AI models, 2 ambient AI pilots, and reviewed 33 nomograms.
  • Developed and shared novel AI governance tools and an "Express Pass" methodology.
  • Identified key lessons learned and open research questions in AI governance for oncology.

Conclusions:

  • This report presents one of the first comprehensive, large-scale Responsible AI governance models in oncology.
  • The developed model and tools provide a scalable framework for managing AI in cancer care.
  • Further research is needed to address evolving AI technologies and their governance in oncology.