Radiology Co-Pilot

AI-DRIVEN ANNOTATION

Intelligent AI assistant automates repetitive tasks, provides real-time annotation guidance and produces higher quality training data.

Annotation Co-Pilot

Domain Knowledge working with you

iMerit’s Radiology Annotation Co-Pilot is designed to enhance and simplify your medical image data annotation process for greater efficiency and accuracy at lower cost. The real-time annotation assistant supports radiologists by automating repetitive tasks, providing real-time guidance, and ensuring adherence to project-specific guidelines.

Integrated with the Ango Hub platform, the medical image data annotation co-pilot can be fully customized to the unique requirements of your project. Co-pilot increases the speed to ramp, while maintaining accuracy, consistency and compliance to your medical image data annotation guidelines.

Sub-Domain Expertise: Ability to fine-tune and create expert level co-pilots in mammography, cardiology, or musculoskeletal imaging.
Project Customization: Tailor the taxonomy and training to align with project- specific guidelines.
Real-Time Annotation Support: Offers instant suggestions, highlights areas of interest, and reduces the need for manual research.
Guideline Adherence: Ensures consistent application of project rules, reducing errors and rework.
Automated Validation: Compare human and AI-generated annotations with guidelines and ground truth data.
Multimodal Capabilities: Combines text and image analysis using AI tools for contextual and visual understanding.
Context-Aware Guidance: Interprets project instructions to provide customized, context-driven support.

SMART AI ASSISTANT

FASTER, BETTER AND MORE COST EFFECTIVE

GREATER EFFICIENCY

Faster medical image data annotation processes by streamlined workflows and automating repetitive tasks.

IMPROVE ACCURACY

Real-time guidance and validation ensures fewer errors, less rework and consistent quality of output.

FASTER DEPLOYMENTS

Accelerate your project completion, enabling quicker delivery and faster time-to-market.

Talk to an Expert

Intelligent Automation

Supporting full life cycle

PRE-LABELING

Medical image annotation co-pilot automatically generates initial annotations and classifications based on guidelines.

INTERACTIVE

Allows medical image annotators to query guidelines and annotations in real-time for instant feedback.

QC & ANALYTICS

Evaluate, compare and score data annotation outputs for accuracy and optimize for quality.

"Co-Pilot is a transformative AI tool designed to empower annotators by reducing cognitive load and improving output quality."

Sina Bari, AVP of Healthcare AI

Who Benefits?

RADIOLOGISTS

Reduced workload, increased productivity, and improved annotation quality. Applying their expertise and knowledge where it is needed most.

DEVELOPERS

Improved data accuracy and high-quality data annotations aid in better models for diagnostics and treatment planning.

MEDICAL IMAGING COMPANIES

Faster to market at a lower cost through better workflow reliability, and customized solutions for diverse projects.

56

Decrease in Annotation Time

70

Savings in Supervisor QC

38

Increase in Accuracy

MEDICAL IMAGING DATA ANNOTATION

CO-PILOT USE CASES

FASTER GUIDELINE RESEARCH

A Radiology Manufacturer developing a breast cancer detection model wants to have raters refer to the American College of Radiology guidelines for direction on lesion classification. The co-pilot is fine-tuned for this input and lets radiologists reference domain knowledge without costly research time. This improves turnaround time and reduces risk of costly rework.

EDGE CASE SOLUTIONING

Edge-cases in the data are the most valuable when training a model. By identifying which errors in data annotation are happening more frequently than would be expected by random occurrence alone, Developers and Data Managers can identify edge cases and opportunities for guideline clarification. Co-pilot acts as a dataset tool to bring advanced analytics to data operations.

IMPROVED MEDICAL CODING

Medical coding often has areas of nuance that are difficult to capture in reference material alone. Using a fine-tuned domain model, our copilot can interact with the data in dialogue to capture and communicate information that often is buried in forums, updates, and newsletters.