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.
Most deep learning systems for cancer grading are trained the same way an image classifier is trained to tell cats from dogs. Each tissue patch gets a single label, benign, well differentiated, moderately differentiated or poorly differentiated, and the network…
DIOR-VIT: Vision Transformers Learn the Order of Cancer Grades Read More »
Prostate cancer is, by the paper’s own framing, the second most frequently diagnosed cancer and the fifth leading cause of cancer death among men worldwide. Grading how aggressive a tumor looks under the microscope, a task pathologists perform using a…
Every convolutional network builds a stack of feature maps as an image moves through its layers. The early layers see fine texture, edges, small color patches, the kind of detail a dermatologist notices when zooming in on a lesion border.…
SPCB-Net And Skin Cancer Detection Explained Read More »
Super resolution ultrasound, also called ultrasound localization microscopy, works by injecting a patient or an animal with microbubble contrast agents and then watching where those bubbles go as blood carries them through the smallest vessels in an organ. Each bubble…
How Multi Frame Deconvolution Sharpens Super Resolution Ultrasound Read More »
Take a single brain MRI from a fifty year old and ask what that same brain will look like at seventy, both if the person ages normally and if they develop Alzheimer’s. Answering means bending the anatomy forward in time…
InBrainSyn, Individual Brain Aging From One MRI Scan Read More »
Retinal fundus photographs are one of the more quietly useful tools in modern medicine. A single photograph of the back of the eye, taken through the pupil with no incision and no injection, can reveal signs of diabetic retinopathy, but…
Generative Models: A Causal Fix for Shortcut Learning in Retinal AI Models Read More »
Contrast enhanced imaging is genuinely useful, which is exactly why it became standard practice. In MRI, gadolinium based agents make the difference in signal between tumor tissue and normal tissue much easier to see, and in CT, iodinated contrast media…
A Contrast Free Tumor AI Owes Little to Its Text Model Read More »
Foundation models trained the way large language models are trained have gotten good at a strange range of things at once, reasoning across images and text, summarizing dense documents, and even segmenting regions in an image when guided by a…
How RO LMM Plans Breast Cancer Radiotherapy End to End Read More »
This article explains and critically evaluates a piece of published research. It is not medical advice, a diagnostic tool, or a treatment recommendation. Nothing here should inform an actual clinical decision. Readers with questions about a medical condition or an…
SVIS-RULEX Explains Medical AI Decisions, But Its 26 Features Have a Math Problem Read More »
Picture a cardiologist staring at a 12 lead ECG trace from a patient being considered for cardiac resynchronization therapy, the kind of device that paces both sides of the heart back into rhythm. The tracing tells her something is off.…
Why Cardiac Digital Twins Can’t Be Solved From One ECG Read More »