Models in this group take a 3D CT volume, some with report or question text alongside. Each table gives three hardware tiers — Minimum, the smallest configuration on which the model runs correctly; Medium, the usual production configuration; and High, a configuration sized for peak volume. The rate is what one server of that tier processes per hour. Use these figures for initial sizing only. Before production we benchmark your own data to confirm accuracy, latency, throughput and cost.
CT-FM
Vendor: Project Lighter
What it does: produces a general-purpose representation of a CT volume that triage, retrieval and segmentation heads all reuse — so one pass over the archive serves several endpoints instead of one.
Task-dependent across segmentation, triage and retrieval benchmarks.
Merlin
Vendor: Stanford MIMI
What it does: reads a CT volume alongside the clinical text that accompanies it, and returns findings, masks, report text and prognosis. The broadest single deployment on this page.
Task-dependent across CT classification, segmentation, retrieval, report generation and prognosis.
CT-CLIP
Vendor: CT-CLIP authors
What it does: indexes CT volumes against report text so an archive can be searched by description, and a new finding scored without a training set for it.
Task-dependent across CT classification and retrieval benchmarks.
CT-CHAT
Vendor: CT-CLIP authors
What it does: answers questions about a CT volume in plain language, which suits review and teaching rather than routine reporting.
Task-dependent across CT visual question answering.
RADAR
Vendor: Alibaba DAMO Academy and collaborators
What it does: diagnoses across abdominal anatomy from a contrast-enhanced CT, and is one of the few models here with multi-centre external validation — AUC 0.895 across eight sites.
External-centre AUC 0.895 across eight centres; emergency CT AUC 0.904 in the reported Science study.
LiON
Vendor: Alibaba DAMO Academy and collaborators
What it does: classifies liver malignancy and outlines the lesion, validated in a multicentre liver CT study. A single-indication model, and stronger inside that indication than any general one.
Task-specific performance reported in a multicentre liver-CT study; no single universal metric.
Percival
Vendor: Percival authors
What it does: pairs a CT volume with its report to support retrieval, classification and prognosis from the same index.
Task-dependent across retrieval, classification and prognosis benchmarks.
Pillar-0
Vendor: Yala Lab
What it does: covers an unusually wide finding list across CT and MRI — reported best-performing on 319 of 366 findings in its own study, which is a claim worth testing on your data before it is relied on.
Reported as best-performing model on 319 of 366 RATE findings in its study; accuracy varies by finding.
SPECTRE
Vendor: SPECTRE authors
What it does: returns embeddings, segmentation and retrieval scores from a CT volume with optional text, covering three task types from one model.
Task-dependent across CT classification, segmentation and retrieval.
TAP-CT-B-3D
Vendor: TAP-CT authors
What it does: produces a task-agnostic 3D CT representation, intended as the base layer under your own segmentation or classification head.
Task-dependent across CT segmentation and classification benchmarks.
3DINO
Vendor: AICONS Lab
What it does: handles both CT and MRI volumes in one 3D representation model, which avoids maintaining a separate encoder per modality.
Task-dependent across 3D classification and segmentation datasets.
Curia-B
Vendor: Raidium
What it does: reads CT and MRI DICOM directly and returns general radiology embeddings — a practical base where your inputs arrive straight from PACS.
Task-dependent; general-purpose radiology representation model.
CT Foundation
Vendor: Google Health
What it does: compresses a CT volume into a 1408-dimension embedding, so a classifier trained on a few hundred labelled studies becomes viable.
Data-efficient 3D CT classification from embeddings; research-endpoint origin, now downloadable (developer-reported).