In the high-stakes world of clinical research, fixed machine learning models face a silent, inevitable threat: drift. As clinical practices shift, lab equipment is recalibrated, and patient populations evolve, static algorithms slowly degrade in accuracy.

If a patient's kidneys aren't clearing waste efficiently, p-tau217 levels in the blood naturally back up and spike—creating a terrifying "false positive" for Alzheimer’s pathology in patients who are cognitively healthy.

At Glassbury AI, we believe true precision medicine requires algorithms that understand basic biology—including the plumbing.