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Adam Hochstetler

What I Learned about Radiology Residents and AI at the Perelman School of Medicine (UPenn)

Adoption, Read Time, Turnaround Time, Workflow

Whether fearful or excited in nature, discussion around the potential impacts of AI on healthcare remains a very hot topic throughout healthcare facilities across the US. Recently, I was invited by my friends at Technology Crossover Ventures to speak on a panel at the Perelman School of Medicine at the University of Pennsylvania on just this topic – my perspectives of AI in healthcare. I was also joined by industry peers William Boonn of Rad AI and Nick Gossen of Dandelion Health. We debated with a live audience of radiology residents and the takeaways from the conversation left me with both surprising and unsurprising insights.

Nearly seven years ago, computer scientist Geoff Hinton famously quoted that AI is going to replace radiologists within five years. Of course, the concern that high sensitivity and high specificity algorithms would render human radiologists obsolete has not come to fruition. Yet, I figured that the fear likely remained amongst radiology residents, or so I thought. Upon conversing with the residents of UPenn, this was not their first, second, or third fear; in fact, they almost laughed at the idea.

Instead, the radiology residents granted me a different thought-provoking concern to ponder on as we contemplate the future of AI, not only in healthcare, but beyond:

  • Is AI increasing efficiency just for the sake of efficiency? In other words, it’s great that AI can make them more productive as radiologists, but is that only going to cause a downward spiral where the goalposts shift and put them under similar pressure to what they would face without the assistance of AI?
    • Theoretically, if AI could eventually rule out certain subsections of normal studies (which does have clearances in parts of Europe), radiologists would not spend much time on normal studies. On the surface, that seems like a major boost in efficiency, but residents pointed out that it would take away the “mental break” that comes with reading normal studies. The concern here is that only handling complicated cases could cause more mental fatigue and burnout. 

Radiology Residents want to lead the healthcare AI revolution

Although we’re constantly surrounded by AI conversations internally at Aidoc and externally, the realist would point out that healthcare AI is still a new technology and has its fair share of skeptics. With that said, there is a mountain of clinical evidence that testifies to the effectiveness of AI in health systems. We believe at Aidoc that its widespread adoption is more a matter of “when” than “if.” The residents at UPenn understood that, and led the conversation in an interesting direction:

  1. The new generation of practitioners are interested in assuming leadership roles in AI adoption
  2. Radiology residents have an opportunity to lead the healthcare AI revolution rather than run the risk of other departments using AI to circumvent them

Overall, the conversation was more ‘excited’ versus ‘fearful’ in my eyes, and gave me a lot of optimism for the future of AI in radiology as it’s clear the incoming cohort of radiologists are acutely aware of the technological revolution, and in that, are ready to embrace the healthcare AI revolution…with a few caveats.

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