E5141. Business Facing Approaches to Artificial Intelligence in Radiology
  1. Michael Gilbert; Boonshoft School of Medicine
  2. Reid Fursmidt; Boonshoft School of Medicine
  3. Robert Short; Boonshoft School of Medicine; Dayton Veterans Affairs Medical Center
The integration of artificial intelligence (AI) into the field of radiology has garnered significant attention, driving numerous enterprises to actively pursue the commercialization of AI technologies. This educational submission examines the pivotal role businesses play in facilitating and propelling AI integration. This introduction to AI will equip learners with the basic knowledge necessary to understand not only the role AI may play in their career in medicine but also how it is to be forged through regulation and desire for profitability. The exploration broadly encompasses two closely intertwined dimensions: workflow applications and diagnostic approaches. The discussion delves into ethical and legal guidelines established by the FDA, serving to regulate AI-based tools categorized as medical devices or Software as Medical Devices (SaMD). Notably, this regulatory framework has heightened business interest and catalyzed financial investments, thereby accelerating the adoption of AI within the radiology landscape. Through this presentation, we aim to foster an understanding of the multifaceted influence of both regulation and industry in shaping the trajectory of AI integration in radiology.

Educational Goals / Teaching Points
Understand and cite examples of businesses that are actively engaged in working on the integration of AI into radiology. Differentiate between AI-derived workflow applications and diagnostic methodologies in radiology. Review the ethical and legal guidelines by the FDA pertaining to the designation SaMD regulation of AI-based tools. Understand potential business roles in the adoption of AI in radiology, including funding, market strategies, promotional efforts, and technical infrastructure.

Key Anatomic/Physiologic Issues and Imaging Findings/Techniques
Not applicable.

The FDA's ethical guidelines have fueled heightened AI algorithm approvals and increased business investments. The trajectory of AI in radiology hinges on the collaboration between academic institutions and AI-driven healthcare enterprises.