10 DAILY MICCAI Tuesday Poster Presentation separate paper, he demonstrated that incorporating MRI information leads to more accurate outcome predictions. Therefore, using MRI should lead to better counterfactual outcomes. Now, by harnessing the power of deep learning on MRI data, he could significantly enhance his model for causal inference. Imagine a scenario where a patient walks into a doctor’s office, experiencing many symptoms that defy easy categorization. The doctor collects information about the patient, possibly conducting a series of MRIs to gain deeper insights. Traditionally, making a diagnosis and determining the appropriate treatment course would be a formidable challenge due to the uniqueness and complexity of the patient’s condition. However, this process becomes more systematic and informed thanks to Joshua’s work. “Because this patient is unique and has these complex features, the doctor can see which drugs would work well for them,” he explains. “These work, these might work, these don’t work. They can discuss the risks of each drug, balance the costs, and make an informed decision about how to treat this patient best.” Looking to the future, while the current focus is on static treatment decisions made at a single point in time, Joshua sees a clear path toward dynamic treatment decisions involving assessing a patient’s response to a particular drug over time. If it does not yield the expected results or thepatient’s condition changes, the model could help doctors decide whether to switch or adjust treatments. This dynamic approach aims to devise a comprehensive profile of each patient’s health journey, ensuring that treatment decisions evolve as new information becomes available. “I am hoping that this paves the pathway for deep learning and
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