Clinical-grade AI. Privacy-first. Pure .NET.
Build HIPAA-compliant medical imaging analysis, clinical NLP, and patient analytics systems. Federated learning keeps patient data on-premises while training globally effective models.
Challenges we solve
Patient data cannot leave hospital networks
Federated learning trains models across institutions without centralizing data. Differential privacy guarantees individual patient protection.
Python environments in clinical IT infrastructure
AiDotNet integrates natively with .NET-based hospital systems (Epic, Cerner integrations). No Python dependency to manage.
Regulatory approval for AI-assisted diagnostics
On-premises deployment, full audit trails, model versioning, and explainability tools support FDA/CE regulatory submissions.
Fragmented tools for medical imaging and clinical NLP
Unified library covering medical image segmentation (MedSAM), clinical NER (BioBERT), radiology report generation, and drug interaction analysis.
Key capabilities
Medical Image Analysis
Segment tumors, detect pathologies, and analyze radiology images with specialized models.
Clinical NLP & NER
Extract diagnoses, medications, procedures, and lab values from clinical notes and EHRs.
Federated Learning
Train models across hospitals without sharing patient data. HIPAA and GDPR compliant.
Drug Discovery & Molecular
Predict molecular properties, drug interactions, and protein structures with GNN models.
Patient Risk Prediction
Predict readmission, deterioration, and treatment outcomes from clinical data.
Medical Document Processing
Digitize medical records, prescriptions, and lab reports with OCR and document AI.
Typical workflow
Data preparation
De-identify patient data and prepare DICOM images, clinical notes, and lab results.
Model selection
Choose appropriate models: MedSAM for imaging, BioBERT for clinical NLP, GNNs for molecular data.
Federated training
Train across hospital sites using FedAvg with differential privacy guarantees.
Validation & explainability
Validate on held-out data with clinical metrics. Generate attention maps and feature importance.
Regulatory documentation
Export model cards, performance reports, and audit trails for FDA/CE submission.
On-premises deployment
Deploy within hospital network using AiDotNet.Serving with full access controls.
Healthcare AI with AiModelBuilder
using AiDotNet;
// Medical image segmentation with AiModelBuilder
var segmentModel = await new AiModelBuilder<float, float[], float>()
.ConfigureModel(new MedSAM<float>("medsam-vit-base"))
.ConfigureOptimizer(new AdamOptimizer<float>())
.ConfigurePreprocessing()
.BuildAsync(medicalImages, segmentationMasks);
var tumorMask = segmentModel.Predict(ctScan);
// Clinical NER with AiModelBuilder
var nerModel = await new AiModelBuilder<float, float[], float>()
.ConfigureModel(new ClinicalBERT<float>())
.ConfigurePreprocessing()
.BuildAsync(clinicalNotes, entityLabels);
var entities = nerModel.Predict(newClinicalNote);
// Privacy-preserving federated training
var fedModel = await new AiModelBuilder<float, float[], float>()
.ConfigureModel(new NeuralNetwork<float>(
inputSize: 512, hiddenSize: 128, outputSize: 5))
.ConfigureFederatedLearning(new FederatedOptions(
strategy: new FedAvg<float>(), rounds: 50,
privacyBudget: new DifferentialPrivacy(epsilon: 1.0)))
.BuildAsync(features, labels);