What You Should Know:
– a2z Radiology AI has raised $4.5M in seed funding led by Khosla Ventures and SeaX Ventures to accelerate the rollout of its FDA-cleared triage system. Unlike existing tools that flag single conditions, a2z’s platform simultaneously triages seven urgent conditions on abdomen-pelvis CT scans, aiming to replicate the comprehensive workflow of a human radiologist.
– New clinical data presented at RSNA 2025 shows the technology reduces reporting time by nearly 18% while boosting radiologist confidence.
a2z Radiology AI Secures $4.5M to Build the “Generalist” Imaging Engine
For the better part of a decade, radiology AI has been defined by fragmentation. Hospitals have been forced to adopt a patchwork of “point solutions”—one algorithm for detecting lung nodules, another for intracranial hemorrhage, and yet another for rib fractures. While effective in isolation, this disjointed approach has failed to mirror the holistic way radiologists actually work.
a2z Radiology AI is betting it can solve this fragmentation problem. The company announced today a $4.5M seed funding round with participation from heavyweights Khosla Ventures and SeaX Ventures. The capital injection will fuel the commercial deployment of a2z’s FDA-cleared a2z-Unified-Triage system and advance its R&D toward broad-spectrum imaging interpretation.
This investment validates a distinct shift in the medical AI market: a move away from narrow, single-task algorithms toward comprehensive systems capable of evaluating complex studies from “A to Z.”
The “Everything” Algorithm for High-Volume CTs
The core of a2z’s value proposition is its ability to handle complexity. The company recently received FDA clearance for a2z-Unified-Triage, the first system in the U.S. market capable of simultaneously triaging seven distinct urgent conditions on abdomen-pelvis CT scans.
This creates a massive efficiency lever for health systems. Abdomen-pelvis CTs account for over 20 million exams annually in the U.S., making it the highest-volume CT category. By bundling detection capabilities into a single workflow, a2z aims to act less like a notification tool and more like a true intelligent assistant.
Quantifying Efficiency: Data from RSNA 2025
The funding follows a successful debut at RSNA 2025, where a2z presented the first prospective study evaluating AI-assisted preliminary report drafting for abdomen-pelvis CTs.
The results offered a rare, quantified look at how comprehensive AI impacts the radiologist’s daily grind. The study found that AI assistance delivered:
- 17.8% reduction in reporting time.
- 14.8% increase in radiologist confidence.
- 22.4% decrease in mental demand.
Crucially, the study noted improved detection of findings without a corresponding spike in false positives—a notorious pain point that has historically slowed AI adoption in clinical settings.
“The response at RSNA validated what we’ve been building—AI that considers everything from A to Z in each study,” said Pranav Rajpurkar, PhD, co-founder of a2z Radiology AI and Associate Professor at Harvard Medical School.
