Yeah, I think it’s important to realize that the AI tools are sort of really at a very early stage in the context of neurology. For these tools to be able to be translated into the real world and to be integrated into a clinical neurology practice, we need to really carefully think about how to evaluate these tools. For example, not only think about how these tools are robust in terms of making those predictions across different patient populations...
Yeah, I think it’s important to realize that the AI tools are sort of really at a very early stage in the context of neurology. For these tools to be able to be translated into the real world and to be integrated into a clinical neurology practice, we need to really carefully think about how to evaluate these tools. For example, not only think about how these tools are robust in terms of making those predictions across different patient populations. You know, for example, if you’re training the AI model on data from the US population, you want to make sure the model is also accurately able to predict, you know, somebody who is outside the US, right? So you want to build those very robust AI methods and models that can be generalizable, robust. Second thing is to really think about how to evaluate these models in the real world. In the real world, what happens is that not every patient will have all forms of data available, which means if the AI model is actually taking information that is whatever is available in a clinical setting, it should be able to take that information and should be able to make that prediction. In other words, if there is some missing information, the AI model should be able to capture the patient-level characteristics even in the presence of missing data and still be able to make that prediction. Plus we also need to think about how to validate these models. For example, if I take this AI model and then compare it with some experts, then what we are trying to do is we’re trying to do this head-to-head validation of these AI models to see if the AI models actually at least meet that clinical standard. So we are taking all these different steps to really think about how to critically evaluate these AI models, not just trying to build some fancy AI methods, but also making sure that these AI models are robust, meet the clinical standards, and also actually do the job in sort of this kind of a wide variety of scenarios that exist.
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