{"slug":"radiology","tag":"radiology","variants":["radiology"],"description":"No description yet","count":8,"kinds":{"person":1,"technology":7},"related":[{"slug":"foundation-model","tag":"foundation-model","shared":5},{"slug":"risk-model","tag":"risk-model","shared":2},{"slug":"lung","tag":"lung","shared":1},{"slug":"screening","tag":"screening","shared":1}],"records":[{"id":"denise-aberle","kind":"person","name":"Denise R. Aberle","route":"/people/denise-aberle/","tldr":"Principal investigator of the National Lung Screening Trial, which first proved CT screening saves lives."},{"id":"merlin-ct","kind":"technology","name":"Merlin (Stanford abdominal CT vision-language model)","route":"/technologies/merlin-ct/","status":"emerging","tldr":"Merlin is a model trained on 15,000 CT scans with their reports that can find and describe hundreds of findings."},{"id":"radfm","kind":"technology","name":"RadFM (generalist radiology foundation model)","route":"/technologies/radfm/","status":"emerging","tldr":"An open generalist model that answers questions about 2D and 3D scans."},{"id":"ct-fm","kind":"technology","name":"CT-FM (whole-body CT foundation model)","route":"/technologies/ct-fm/","status":"emerging","tldr":"A model pretrained on 148,000 CT scans to segment organs and triage findings."},{"id":"medsam","kind":"technology","name":"MedSAM / SAM-Med3D (segment anything for medicine)","route":"/technologies/medsam/","status":"emerging","tldr":"Adaptations of Meta's Segment Anything model that outline tumours and organs on any scan with a click."},{"id":"sybil","kind":"technology","name":"Sybil (MIT/MGH lung cancer risk from CT)","route":"/technologies/sybil/","status":"emerging","tldr":"Predicts a person's six-year lung cancer risk from one low-dose CT, even when no nodule is visible."},{"id":"mirai","kind":"technology","name":"Mirai (MIT breast cancer risk from mammograms)","route":"/technologies/mirai/","status":"emerging","tldr":"Reads a mammogram to estimate five-year breast cancer risk, consistently across races and devices."},{"id":"aidoc-care","kind":"technology","name":"Aidoc CARE (clinical radiology foundation model)","route":"/technologies/aidoc-care/","status":"emerging","tldr":"Aidoc CARE is one radiology foundation model, pretrained on CT scans without labels, whose task-specific heads have each been FDA-cleared to flag urgent findings in emergency scans so radiologists read those first. Its oncology relevance is indirect, catching incidental masses; the regulatory evidence covers triage, not diagnostic accuracy for tumours."}]}