Voio provides a vision‑language AI platform that ingests CT, MRI, X‑ray and ultrasound studies and generates draft radiology reports with context‑aware autocomplete suggestions. The system reduces report turnaround time while delivering specialist‑level detection for generalist radiologists, and offers open‑source models and APIs for integration with PACS/RIS workflows.
Funding
Funding not disclosed
Founders
Product
Problem
Radiology reporting workflows are fragmented and labor‑intensive, requiring radiologists to manually synthesize image findings into narrative reports. This process slows diagnosis, especially for generalist readers who may lack specialist expertise, and limits the ability to shift from reactive to proactive disease management.
Solution
Voio delivers a vision‑language AI platform that ingests imaging studies across all modalities and protocols, then generates draft reports with context‑aware autocompletions derived directly from pixel‑level analysis. By unifying the reporting pipeline, the system reduces report turnaround time while preserving diagnostic quality. The models are trained and validated on data from over 90 hospitals, enabling generalist radiologists to achieve specialist‑level detection of subtle findings. Open‑source model releases ensure transparency and facilitate academic benchmarking, supporting a transition toward proactive disease insight in radiology practice.
Target Audience
The primary customers are radiology departments in hospitals and imaging centers, including both subspecialist and generalist radiologists who need faster, high‑quality report generation across all imaging modalities.
Features
- Vision‑language models that process CT, MRI, X‑ray, and ultrasound studies to extract findings and suggest narrative text in real time
- Smart autocomplete engine that populates report sections (e.g., impression, findings) within seconds, reducing manual typing
- Clinician‑led development workflow that incorporates radiologist feedback to align AI output with clinical terminology and standards
- Open‑source model distribution for independent validation, reproducibility, and integration with existing PACS/RIS environments
- Large‑scale validation across 90+ hospitals in 30 countries, providing evidence of robustness across diverse imaging protocols
- API and SDK for seamless embedding into radiology information systems, supporting automated report generation and workflow orchestration
- Proactive insight module that flags early disease patterns based on learned longitudinal risk signatures