Getting Started with OncoShield AI
Integrate industry-leading oncology diagnostic intelligence into your clinical workflow. Our Artificial Neural Network (ANN) models provide real-time malignant detection with 99.8% precision, designed specifically to assist oncologists in making rapid, evidence-based decisions.
Whether you are integrating our system into an existing EHR (Electronic Health Record) software or building a standalone diagnostic portal, this documentation covers everything from basic system architecture to advanced webhook implementations.
System Overview
The OncoShield AI engine utilizes a multi-layered diagnostic pipeline. The process ensures that raw medical imagery is properly cleaned and standardized before passing through the deep learning classification model.
End-to-End Diagnostic Pipeline
Pre-processing
DICOM/SVS images are normalized, artifacts removed, and regions of interest (ROI) are dynamically cropped to ensure zero interference from external noise.
Feature Extraction
Morphological parameters (Radius, Texture, Perimeter, Area, Smoothness) are extracted automatically using the core Convolutional layers.
Classification
The ANN outputs a probability distribution across Benign/Malignant classes with a confidence threshold, attaching clinical protocol recommendations.
Validated Clinical Accuracy
Our model has been rigorously validated against historical datasets containing over 50,000 anonymized breast cancer screening cases. The precision matrix below illustrates the latest Q3 2025 Retrospective Study findings.
Model Precision Matrix
Q3 2025 Retrospective Study
AI Philosophy
We employ a "Human-in-the-Loop" architecture, ensuring AI findings are always presented as second opinions for clinical confirmation. AI should assist, not replace, medical experts.
API Reference
Use our RESTful API to submit pathology slides and receive immediate diagnostic analysis. The API supports batch processing and asynchronous inference for high-volume hospitals.
import oncoshield_ai
# Initialize the clinical client
client = oncoshield_ai.Client(api_key="YOUR_KEY")
# Submit biopsy scan
analysis = client.diagnostics.analyze(
source_path="./slide_001.svs",
modality="histopathology",
priority="stat"
)
print(analysis.confidence_score){
"id": "diag_982hjs82",
"status": "completed",
"result": {
"classification": "MALIGNANT",
"confidence_score": 0.954,
"roi_coordinates": [120, 450, 80, 80]
},
"clinical_protocol": [
"Requires biopsy",
"MTB Review"
]
}Webhooks
Configure webhooks to receive real-time HTTP POST notifications when a diagnostic analysis is complete. This avoids the necessity of active polling of our API, reducing server load on your end.
| Event Name | Description |
|---|---|
| analysis.completed | Triggered when the AI successfully processes a case and classification is ready. |
| analysis.failed | Triggered if image quality is too low for inference or metadata is missing. |
| model.retrained | Triggered when a new model weight snapshot is deployed to production. |
SDKs & Libraries
We provide officially supported SDKs for Node.js and Python to speed up your integration process. These libraries handle automatic retries, authentication formatting, and payload compression out of the box.
npm install @oncoshield/node-sdkcontent_copypip install oncoshield-aicontent_copyData Privacy
Patient data security is our highest priority. All PHI (Protected Health Information) is automatically de-identified before entering our inference engine. Raw scans are kept entirely within memory during inference and are permanently purged upon response generation, ensuring zero data retention.
End-to-End Encryption
All transit traffic is secured via TLS 1.3. At rest (during the brief inference window), data is shielded using AES-256 encryption. We utilize strictly isolated virtual private clouds (VPCs) to ensure cross-tenant data boundaries.
Regulatory Standards
OncoShield AI adheres strictly to international regulatory frameworks. Our clinical validation studies have been submitted to the FDA under the Software as a Medical Device (SaMD) categorization framework. Furthermore, our infrastructure is independently audited annually to maintain our ISO 27001 certification for information security management.
HIPAA Compliance
For institutions operating within the United States, OncoShield AI operates as a compliant Business Associate. We provide comprehensive Business Associate Agreements (BAAs) upon enterprise onboarding. Our architecture is designed from the ground up with strict Access Controls, Audit Controls, and Integrity mechanisms matching the strict guidelines of the HIPAA Security Rule.