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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Governance

Background:

  • Generative AI tools are rapidly entering the radiology practice setting.
  • Existing regulatory pathways struggle to address the unique challenges posed by these general-purpose technologies.
  • Concerns exist regarding liability, privacy, and clinical risks associated with generative AI.

Purpose of the Study:

  • To propose a comprehensive governance model for generative AI in radiology.
  • To address the gaps in oversight for these rapidly evolving technologies.
  • To provide radiologists with a risk management strategy.

Main Methods:

  • Development of a trilaminar governance model.
  • Integration of federal regulations, institutional guidelines, and industry self-regulatory frameworks.
  • Analysis of existing FDA premarket review limitations.

Main Results:

  • A proposed trilaminar governance model offers a multilayered approach to AI oversight.
  • This model integrates federal regulations as a foundational scaffold.
  • Additional tiers of institutional guidelines and industry self-regulation create a comprehensive system.

Conclusions:

  • The trilaminar governance model provides an effective risk management strategy for generative AI in radiology.
  • This framework aims to foster continued technical development while ensuring patient safety.
  • Implementing this model promotes responsible innovation and enhances patient care.