NewVue is an AI-first healthcare technology initiative focused on modernizing radiology workflows through AI radiology reporting, real-time transcription, and structured report generation. Designed for radiologists and care teams, it enables faster, more accurate clinical documentation while ensuring compliance, scalability, and seamless integration with existing hospital systems.
Problem: limited visibility and slow insights in radiology workflows
Before implementing a dedicated AI radiology reporting solution, radiology workflows were fragmented and manual. Radiologists lacked real-time visibility into report completeness, turnaround times, and dictation quality, while operations teams had limited insight into session-level reporting performance.
Engineering teams relied on logs and ad hoc checks to track transcription accuracy and system health. As study volumes and concurrent dictation sessions increased, these siloed workflows became inefficient, error-prone, and difficult to scale driving the need for NewVue to adopt a secure, real-time AI radiology reporting platform that unified clinical and operational visibility without adding complexity.
Solution: an AI-driven healthcare data analytics platform
GoML designed and delivered NewVue using its Agentic AI boilerplate for real-time clinical workflow orchestration and radiology reporting. The platform captures live speech, transcription confidence, and field-level progress to manage end-to-end radiology dictation workflows.
Powered by generative AI on AWS Bedrock, the Agentic AI layer interprets dictation context, drives field-by-field progression, applies real-time corrections, and generates structured radiology reports giving clinical and technical teams instant visibility without disrupting workflows.
Natural language intelligence for AI radiology reporting
Radiologist interact with the AI radiology reporting system through intuitive, role-specific views and AI-generated summaries.
Clinical and operational questions such as report completeness, turnaround delays, or transcription confidence are answered by AI models that understand context and surface meaningful insights from live dictation sessions and stored radiology reports.
Secure execution for clinical AI reporting
Security is embedded across the AI radiology reporting platform.
Instead of exposing raw databases, all reporting insights are generated through validated APIs backed by session-level controls. Sensitive patient data remains protected while enabling trusted visibility for authorized users in HIPAA-compliant environments.
Real-time AI radiology reporting insights
NewVue delivers real-time AI-driven reporting insights, including:
- Dictation progress and field-level report completion
- Transcription confidence and correction trends
- Radiology report turnaround metrics
- Session health and system performance indicators
These insights allow radiology teams to act immediately rather than relying on delayed or static reporting.
Role-based access in AI radiology reporting
The AI radiology reporting platform is designed around real user roles:
- Radiologists access live dictation status, partial reports, and correction feedback
- Engineering teams monitor transcription latency, API health, and AI model performance
Each role sees only what is relevant, secure, and authorized.
User interface and system integration
A modern React-based interface allows users to authenticate securely, view AI radiology reporting dashboards, and track real-time reporting activity.
The platform integrates seamlessly with NewVue’s AWS-based backend and exposes APIs for integration with hospital systems, PACS, and downstream radiology reporting tools.
Impact
- 60–70% reduction in manual monitoring and reporting efforts
- 50% faster visibility into report completion and dictation progress
- 2× improvement in insight availability across clinical, admin, and engineering teams
- Higher report consistency through structured, template-driven analytics
About
Before Gen AI vs after Gen AI
“We transformed NewVue's radiology reporting from a reactive, manual process into a real-time, intelligent system that serves clinicians and operations at scale.”
Prashanna Rao, Head of Engineering, GoML
Key takeaways for healthcare organizations
Common challenges
- Clinical data grows faster than manual analytics can handle
- Lack of real-time insights slows decision-making
- Unstructured reporting increases operational risk
Practical guidance
- Build a centralized healthcare data analytics platform
- Design analytics around clinical and operational roles
- Embed security and compliance at the execution layer
Ready to modernize your healthcare analytics?
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