Medical AI brings together our reporting on machine learning for clinical and biomedical problems, from diagnosis and prognosis to medical image analysis and decision support. Because mistakes in this domain carry a human cost, we pay close attention to evaluation, calibration, uncertainty, and the gap between benchmark numbers and bedside reliability. Expect grounded explainers of recent research rather than uncritical product announcements.
A radiologist scrolling through a stack of MRI slices at two in the morning does not have time to trace every millimeter of a glioma by hand. Neither does a technician reviewing a colonoscopy video looking for a polyp that…
Attention Mechanisms Reshaping Medical Image Segmentation Read More »
Fully retraining a billion parameter foundation model every time it stumbles on a new domain is expensive, slow, and risks quietly erasing the very generality that made the model useful in the first place. A team spanning the National University…
Med-SA Explained, Adapting SAM to Medicine With 2 Percent of Its Weights Read More »