Nearly a decade ago, AI pioneer Geoffrey Hinton predicted that artificial intelligence (AI) would soon outperform radiologists. He even went as far as to suggest that people should stop training them altogether. But the reality today looks very different. For example, at the Mayo Clinic, the radiology workforce has grown by 55% since 2016 and the workforce is projected to continue growing. Rather than taking the job of physicians, AI has become a tool that enhances their capabilities, helping radiologists sharpen images, automate repetitive tasks, and detect abnormalities more efficiently. 

This shift reflects a broader trend across healthcare: moving from speculation about AI's potential to practical, targeted implementation. The focus isn’t on replacement—it’s on collaboration. AI is being used to support clinical decision-making and improve accuracy, not sideline the people making those decisions. 

The industry at large is taking a more grounded approach as well. New frameworks like OpenAI’s HealthBench are part of a larger movement to assess AI tools in realistic clinical contexts, emphasizing the importance of safety and usefulness over novelty. While AI’s role in healthcare is still evolving, what’s clear is that its most impactful applications are those that empower physicians—not replace them—and help deliver better care through partnership between human expertise and machine learning. 

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