To AI or Not to AI? That Is the Question. (Or Is It?)

To AI or Not to AI? That Is the  Question. (Or Is It?) 

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By Jeffrey M. Bundy, Ph.D., CEO, United Imaging Intelligence North America; Terrence Chen, Ph.D., CEO, United Imaging Intelligence North America; and Krista K. Stein,* Contributing Editor, Vice President, Marketing & Communications, United Imaging Healthcare North America *krista.stein@united-imaging.com

As artficial intelligence (AI) continues to grow and generate excitement and mystery equally across the healthcare landscape, this article is geared toward unpacking the foundational terminology and supportive technologies, and demystifying the core AI concepts and what they mean for you and your colleagues. The question we need to ask ourselves is how each of us will use AI in the environments we manage. It’s easy for AI to “happen” to us, as it’s already been happening to us. But the field needs “denoising” so that healthcare administrators can answer a different question: How and Where to AI—because the answer to “When?” is “Now.” 
Recent iterations of AI really began with deep learning. The rapid success of deep learning in imaging drew massive attention to AI’s potential in medicine overall. The rise of large language models opened more frontiers, as these models are now being explored to automate documentation, enhance information retrieval, and assist in clinical decision-making. Today, AI is embedded throughout the entire radiology workflow, trans forming imaging practices from initial patient scanning to treatment guidance. AI has significantly advanced visualization capabilities in medical imaging by enabling photo-realistic, real-time interactive rendering. Nowa days, AI plays a critical role in virtually every step of the medical imaging pipe line, enabling faster, more accurate, and highly personalized patient care.