Explainable Generative AI for Radiology Reporting
RadSight AI builds clinically deployable vision-language systems that support faster, more consistent reporting—while preserving transparency, auditability, and radiologist oversight.
Built for real radiology workflows
Adoption depends on fit: explainability, integration, and measurable value for radiologists and departments.
Workflow-integrated
Designed to complement reporting workflows rather than add extra steps or separate dashboards.
Explainability first
Transparent outputs with traceable inputs to support clinician trust and responsible deployment.
Measurable efficiency
Faster drafting, reduced report variability, and improved consistency across readers and sites.
A workflow-integrated vision-language platform
A two-stage framework to support structured reporting and impression drafting—while keeping the radiologist in full control.
- Findings-aware impression drafting support
- Structured, section-specific outputs and templates
- Quality assurance and audit support
- Customizable to institutional preferences
- Designed for clinical deployment pipelines
Outcomes that matter to end users
Designed to reduce variability, improve consistency, and support faster reporting—while preserving clinician oversight.
Reduce report variability
Support consistent language and structure across readers, improving communication and downstream care coordination.
Support faster turnaround
Draft impressions and structured outputs can reduce repetitive reporting burden and improve throughput.
Enable QA and peer review
Structured outputs and auditability support quality programs and departmental standardization.
Workflow-aligned adoption
Designed to fit reporting environments and implementation constraints seen in day-to-day practice.
Designed for clinical deployment considerations
Security posture and deployment details are implemented in collaboration with institutional IT and compliance teams.
Access control
Least-privilege access patterns and role-based permissions can be implemented per site requirements.
Secure data handling
Designed to support encrypted transport and storage, with logging and monitoring aligned to clinical environments.
Auditability
Outputs and edits can be captured to support quality review and operational governance.
Note: Specific compliance attestations depend on the final deployment environment and organizational controls.
Colorado-based, clinician-driven AI
RadSight AI LLC is a Colorado-based startup focused on translating advanced academic research in medical AI into clinically deployable, workflow-integrated solutions for radiology. We collaborate with academic and clinical partners to ensure real-world relevance and adoption.
- Clinical pilots and workflow evaluation
- Licensing and commercialization pathways
- Integration planning with enterprise imaging IT
Request a demo or partnership discussion
Tell us a bit about your institution and goals. We'll respond within 1–2 business days.
Email us at nguyenthao2219@gmail.com.
This site describes a clinician-assistive system. Any AI outputs must be reviewed by qualified clinicians and integrated under appropriate institutional oversight.