Week 1: Mary

I just finished the first week of the summer immersion program at Weill Cornell, where I've been shadowing Dr. Prince in the radiology department. As an MD-PhD, he works in research and patient care simultaneously, and it is striking how directly he's managed to connect the two. The problems he encounters with patients become research questions, and the tools his lab builds in response find their way back into his clinical practice.
 
Polycystic kidney disease (PKD) is a good example of how that plays out. Although Dr. Prince's background is in whole-body MRI/radiology, his clinical work with PKD patients drew his attention to a significant problem with how kidney size is measured in these patients. For context, as PKD progresses, the kidneys steadily enlarge as cysts develop, so their total volume essentially reflects how far the disease has advanced. Then, the quick clinical shortcut is to approximate each kidney as an ellipsoid. This practice is fine for a normal, smooth kidney, but in PKD, the kidneys swell into lumpy, irregular shapes covered in cysts, and that approximation breaks down. The most accurate approach is to trace the kidney's outline by hand, slice by slice across multiple cross-sectional views, and add them up; however, this method is rarely used in routine practice because it is very time-consuming.
 
Thus, that's the gap Dr. Prince's software fills by using AI to automatically trace the kidneys and liver (two of the most critical organs in following this disease progression) on each MRI slice, as a radiologist would if they had unlimited time, and then computes the volumes. Because the AI-assisted result is (in theory) both accurate and reproducible at a fraction of the effort, it becomes feasible to track subtle changes in the same patient over months and years.
 
Even though the software has already been rigorously trained on thousands of images, it is still not perfect, as it struggles to pin down the exact edges of each of the organs, making it so the masks it produces aren't quite right out of the box, and Dr. Prince has to go through and correct them by hand to get an accurate measurement. Fixing an almost-finished mask is much faster than tracing one from scratch, but it still requires work. Many clinicians might be tempted to accept the near-perfect masks as they are, and those small, uncorrected errors would only compound as they flow into future measurements and potentially into the model itself.

Overall, this week was very informative. I came into this week half-believing AI was about to replace radiologists, but watching how much still depends on human judgment moved me firmly away from that. What I saw looked more like a partnership than a replacement, and seeing it firsthand was incredibly valuable.

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