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.
Picture a radiology department that wants an AI tool to outline kidneys on ultrasound scans. The best open segmentation model weighs hundreds of megabytes and was trained on everyday photos of cats, cars and coffee mugs. The department has eighty…
Task-Specific Knowledge Distillation for Medical Image Segmentation Read More »
Push a diffusion MRI scanner hard enough and the brain starts to give up secrets it normally hides. Most clinical and research diffusion scans work at roughly one to two millimeters per voxel, which sounds fine until you remember that…
Submillimeter Diffusion MRI With DnSPIRiT Reconstruction Read More »
Cerebrovascular diseases, stroke, moyamoya disease, and cerebral aneurysms among them, remain a major cause of death and disability, and diagnosing them accurately depends on understanding the geometry of the cerebral arteries. Digital subtraction angiography, often shortened to DSA, is the…
DSANET Uses Full DSA Sequences to Segment Cerebral Arteries Read More »
Gliomas are graded by the World Health Organization on a scale that runs from grade 1 to grade 4, with higher numbers marking tumors that grow faster, invade more aggressively, and carry a worse outlook. Ki-67 is one of the…
How An AI Model Reads Brain MRI Scans To Estimate Glioma Grade And KI-67 Read More »
Anyone who has worked on deep learning in healthcare quickly discovers that the academic benchmark setting — one big dataset, one train-test split, one model — rarely survives contact with the real world. Hospital systems accumulate data incrementally. Different sites…
Gleason grading, the system pathologists use to score prostate cancer aggressiveness from tissue samples, has a documented interobserver variability of about 40 percent among trained pathologists looking at the same slide. That number alone tells you the task is genuinely…
Uncertainty Aware Knowledge Distillation for Imbalanced Disease Grading Read More »
Normal pressure hydrocephalus, usually shortened to NPH, shows up mostly in older adults as a triad of gait impairment, urinary incontinence, and cognitive decline, caused by cerebrospinal fluid building up in the brain’s ventricular system. What makes it clinically important,…
Weakly Supervised AI for Normal Pressure Hydrocephalus (NPH) Screening on CT Scans Read More »
Myocardial strain is a measure of how much the heart muscle itself deforms as it contracts and relaxes, not just how much blood volume moves, and that distinction matters clinically. Strain has become a genuinely useful diagnostic signal for heart…
Teaching a Video Memory Network to Watch the Heart Beat Read More »
Inside an interventional radiology suite, an awake patient under regional neck anesthesia swallows involuntarily while a clinician guides a high frequency radiofrequency ablation electrode toward a papillary thyroid microcarcinoma measuring less than ten millimeters across. Every burst of alternating electrical…
DualSwinUnet++ for Thyroid Microcarcinoma Segmentation Read More »
Picture a 96 well plate under a microscope that never blinks. Every twenty minutes for two days it photographs each well, and by the end a single drug has produced about 144 frames of breast cancer cells puffing up, bursting…
AI Screening for Immunogenic Cell Death Inducers Read More »