AI Makes Mistakes. That's Why We Should Embrace It.

AI needs us just as much as we need it.

Last week, I spoke at a digital society meeting about AI in dentistry. I could have discussed the current landscape, the immense potential, or the challenges we face. Instead, I chose to focus on something different: mistakes. Why? Because mistakes aren’t uncommon?

No.

In the technology we use today for clinical decision-making, mistakes are rare. The AI systems approved for clinical practice have already passed rigorous regulatory requirements, ensuring they meet high standards of accuracy and reliability.These advanced products are refined, delivering impressive precision across various dental applications. However, I’m not talking about these well-established, regulated tools.

I’m talking about the mistakes we encounter when we build on these existing functionalities and push into new frontiers—developing beta versions and experimental AI tools still in research. It’s here, in the process of innovation and testing new capabilities, where we inevitably face fresh challenges. And that’s a good thing. These imperfections aren’t setbacks; they are essential to progress.

At first glance, errors might seem like a reason to be cautious or even skeptical about adopting AI technologies. But consider this: every mistake an AI system makes is an opportunity for us to learn, improve, and refine both the technology and our clinical practices. Sometimes, these errors even help us identify hidden biases or reveal insights we didn’t realize were missing.

By embracing these imperfections, we drive innovation and continuous improvement, paving the way for the next leap forward in dental AI.

Embracing the Learning Process

AI doesn’t have intuition. It doesn’t draw on years of hands-on clinical experience or a gut feeling about a tricky clinical situation. Instead, it operates purely on data, algorithms, and learned patterns from previous inputs. While this can lead to incredible precision, it also means that when AI errs—whether by misidentifying a structure or suggesting an incorrect diagnosis—it’s not a random failure. It’s a signal, pointing directly at gaps in the system that need to be addressed.

Why AI Makes Mistakes: A Study Case on Visual Data Analysis..

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