Yeah, of course. So multimodal AI is a very interesting direction. There is the belief that if we integrate data from different modalities, we can better understand the patient’s specific condition. And in this way, we can provide better predictions and better diagnosis. And in general, the concept is very good, but to implement it is very difficult because there are a lot of challenges with multimodal data integration, even with building this large multimodal data set that you need to train the models...
Yeah, of course. So multimodal AI is a very interesting direction. There is the belief that if we integrate data from different modalities, we can better understand the patient’s specific condition. And in this way, we can provide better predictions and better diagnosis. And in general, the concept is very good, but to implement it is very difficult because there are a lot of challenges with multimodal data integration, even with building this large multimodal data set that you need to train the models. So sometimes if the different modalities provide complementary information, it’s fantastic. It leads to better predictions, more accurate models. But sometimes if there are contrasting information or too much noise, it may actually hurt the model performance. So this is like an active direction of research, like when additional modalities really help and when not. And we’ll discuss more in the talk. Key highlights. Oh, okay. So a few key observations, the multimodal data fusion can improve accuracy and also generalization of the models. So you can have more robust models that generalize across patients and populations. We also have new multimodal foundational models, which are good for certain tasks, like for exploring a relation between modalities, but are maybe not necessarily best for prognostic prediction, which is something maybe you would not expect. But it also suggests new avenues how we can improve multimodal models to move forward. And lastly, there is one highlight of the talk is like how the multimodal AI can help with the rare diseases where we don’t have a large amount of data and just bringing all information that we have together can really help with the model predictions or even with designing those models.
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