Overcoming Data Limitations & Biases with Synthetic Image Data

We leverages advanced AI technology created by physician engineers to generate high-quality, diverse, synthetic healthcare images, setting a new standard for accuracy, diversity, and speed in healthcare research and diagnostics.

Amplifying healthcare imaging for breakthroughs that matter, with SynthVision's AI that understands life's nuances.

Values

Paving the way for Healthcare Innovations

Dermatology

Radiology

Histopathology

Endoscopy

Ophthalmology

Significant cost and time saving

Improved Performance

Privacy Protection

Broad Application

Features

Enhancing Imaging Accuracy and Efficiency

Real-World Accuracy

SynthVision captures what real health conditions look like, making the synthetic data more accurate.

Bias Reduction

Generate images for rare disease presentations, rare diseases, and underrepresented groups.

Customizable Data

You can tweak the data to fit your specific needs, e.g. skin color in dermatology, organ variations in radiology.

Data Ownership

You own the data outright, with no worries about privacy issues, patient consent or legal hassles.

case study

Proven Result: HPV

challenge

Lacked data for darker skin tones & certain disease states, leading to reduced performance and bias in the AI model.

result

Achieved

~95% accuracy

in a multi-disease detection AI model.

solution

SynthVision generated synthetic data, creating diverse and rare presentation images.

original

synthvision generated

case study

Enhancing Mammogram Cancer Detection in Asia

challenge

A mammogram detection model, trained on Western data, performed poorly in Asia due to significant differences in breast shape, density, and size.

result

Improved AI model accuracy by

20%

ensuring better detection and care.

solution

SynthVision generated data reflecting the diverse characteristics of Asian women in real-world scenarios.

synthvision generated

case study

Pairing Synthetic Image Generation with Disease Classification Models: Synergistic Machine-Learning Approaches Permit Rapid Development and Deployment of Digital Diagnostic Tools

Read the paper

Contact

How can we help you?

Get in touch

contact@aagee.ai