Unlock the full potential of synthetic patient generation with our state-of-the-art web application. Create detailed synthetic patients through intuitive natural language commands.
Try Live DemoInput patient descriptions in everyday language and let our advanced algorithms generate comprehensive synthetic profiles with realistic medical histories.
Export your synthetic patient data in multiple formats including JSON, CSV, and FHIR. Compatible with all major data analysis tools and platforms.
Access and manipulate your synthetic data directly through Jupyter Notebooks with our Python SDK. Perfect for data scientists and researchers.
Integrate PatientReplicaAI directly into your applications with our comprehensive API. Generate patients programmatically at scale.
Generate patients with complete medical timelines spanning years or decades, including disease progression, treatment changes, and outcomes.
Create entire patient populations with specific characteristics for clinical trials, epidemiological studies, or ML model training.
# Install the PatientReplicaAI SDK
pip install patientreplicaai
# Generate a synthetic patient
from patientreplicaai import PatientGenerator
generator = PatientGenerator(api_key="your_api_key")
patient = generator.create(
"65-year-old male with Type 2 diabetes and hypertension"
)
# Access patient timeline
patient.timeline.visualize()
patient.export("patient_data.json")
POST https://api.patientreplicaai.com/v1/patients/generate
{
"description": "45-year-old female with breast cancer",
"timeline_years": 5,
"include_treatments": true,
"format": "fhir"
}
Response: {
"patient_id": "SYNTH_2025_002",
"data": {...},
"timeline": {...}
}
# Import and initialize
import patientreplicaai as prai
import pandas as pd
import matplotlib.pyplot as plt
# Generate cohort
cohort = prai.generate_cohort(
size=1000,
criteria="adults with cardiovascular disease"
)
# Analyze data
df = cohort.to_dataframe()
df.describe()
# Visualize outcomes
cohort.plot_outcomes()
# Install PatientReplicaAI R package
install.packages("patientreplicaai")
library(patientreplicaai)
# Generate patient
patient <- generate_patient(
"30-year-old pregnant woman with gestational diabetes"
)