AI Medical App Development & FDA SaMD Engineering
Turn deep learning models into clinical medical devices. We build audited, production-ready AI software for radiology imaging, ambient SOAP voice scribing, and predictive patient risk stratification.
Clinical AI Inference Core
Audited SaMD PipelinesMedical AI & SaMD Engineering Capabilities
We bridge biomedical algorithms and rigorous medical device regulation, ensuring your AI product passes regulatory muster and clinician adoption.
DICOM Imaging & PACS AI Integration
Deploy high-throughput DICOMweb inference servers interfacing with hospital PACS/VNA systems. Renders AI segmentations on zero-footprint web viewports.
Ambient Clinical AI Scribing (SOAP)
Multi-speaker diarization and domain-specialized medical speech recognition converting natural doctor-patient dialogues into structured EHR encounter notes in real time.
IEC 62304 Software Lifecycle Controls
Software development plan (SDP), architectural design, risk management (ISO 14971), and software verification & validation (V&V) protocols for Class A, B, and C medical software.
On-Device CoreML & ONNX Edge Inference
Quantize and optimize deep learning models to run offline directly on patient iPhones and Android devices with sub-50ms inference and zero cloud latency.
Predictive Risk Stratification Models
Machine learning models predicting 30-day hospital readmissions, sepsis onset, glycemic excursions, and decompensation episodes from EHR and RPM telemetry.
FDA 510(k) & De Novo Dossier Packaging
Compile comprehensive regulatory software dossiers, cybersecurity documentation, and clinical performance evaluation reports for FDA submission.
Frequently Asked Questions About AI Medical Software
Under FDA and IMDRF guidelines, software intended to be used for diagnostic or therapeutic medical purposes (such as detecting anomalies in radiology scans or calculating drug dosage recommendations) is categorized as Software as a Medical Device (SaMD) and requires formal design controls (IEC 62304).
We implement automated DICOM de-identification pipelines removing all 18 HIPAA Safe Harbor identifiers and burned-in pixel text before ingestion, combined with federated learning architectures where raw PHI never leaves hospital networks.