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Developed an AI-Powered SaMD Platform That Improved Diagnostic Accuracy by 30% in Medical Imaging

Developed an AI-driven SaMD platform to address diagnostic variability and clinician workload by analyzing medical images, surfacing relevant clinical context, and providing explainable recommendations within PACS workflows.

The Clinical Reality We Were Asked to Solve

Project Overview

A diagnostic imaging network was facing rising imaging volumes and a shortage of subspecialty radiologists. Clinicians were spending excessive time reviewing studies, cross-referencing patient history, and documenting findings. Diagnostic accuracy varied between readers, and critical findings were sometimes missed during high-volume periods.

We built an AI-powered SaMD platform from scratch that analyzes medical imaging studies, generates diagnostic recommendations with confidence scores, and surfaces actionable insights within the radiologist's workflow. The platform integrates with PACS and EHR systems via DICOM and FHIR standards, providing real-time decision support without disrupting existing workflows.

What Made This Project Different

SaMD Regulatory Classification Required Rigorous Validation

Unlike general healthcare software, SaMD is regulated as a medical device. We built the platform to comply with IEC 62304 (medical device software lifecycle), ISO 14971 (risk management), and FDA guidance for AI/ML-based SaMD. Every algorithm change required validation against clinical ground truth.

Clinical Decision Support Requires Explainability

Radiologists don't trust black-box AI. We built explainability features that show which imaging features contributed to each recommendation, enabling clinicians to validate AI outputs against their own clinical judgment.

PACS Integration Is DICOM-Complex

The platform needed to query PACS, retrieve DICOM studies, process images, and return results without disrupting radiologist workflow. We built DICOM-aware APIs that handle metadata extraction, image processing, and result routing.

High-Volume Environments Demand Low Latency

During peak hours, radiologists review 50+ studies per shift. The platform needed to deliver AI recommendations within 30 seconds per study to maintain workflow efficiency.

Why Standard Monitoring Solutions Failed Here

Challenges

  • Continuous Data Creates Alarm Fatigue

    Diagnostic Accuracy Variability

    Diagnostic accuracy varied between readers, with miss rates higher during high-volume periods. The platform needed AI-powered decision support to standardize accuracy across readers.

  • Nurses Need Mobile Alerts, Not Station Alerts

    Clinician Cognitive Load

    Radiologists spent excessive time cross-referencing patient history, prior studies, and clinical notes. The platform needed to aggregate relevant context and surface it within the reading workflow.

  • Early Warning Detection Requires ML Models

    PACS Integration Complexity

    PACS systems use DICOM standards with complex metadata. The platform needed to query PACS, retrieve studies, and return results without disrupting existing workflows.

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    Regulatory Compliance for SaMD

    The platform needed to comply with IEC 62304, ISO 14971, and FDA guidance for AI/ML-based SaMD. Every algorithm change required validation against clinical ground truth.

Solution Delivered

AI-Powered Diagnostic Engine

Built deep learning models trained on annotated imaging datasets to detect abnormalities, classify findings, and generate diagnostic recommendations with confidence scores. Models are validated against clinical ground truth.

Explainability Layer

Built explainability features that show which imaging features contributed to each recommendation. Radiologists can validate AI outputs against their own clinical judgment.

DICOM-Aware PACS Integration

Built DICOM-aware APIs that query PACS, retrieve studies, process images, and return results without disrupting radiologist workflow. Results are routed to PACS and EHR via DICOM and FHIR.

Clinical Context Aggregation

Built a context engine that aggregates patient history, prior studies, and clinical notes from EHR, surfacing relevant context within the reading workflow.

Low-Latency Inference Pipeline

Built a low-latency inference pipeline that delivers AI recommendations within 30 seconds per study, maintaining workflow efficiency during high-volume periods.

How the Platform Works

When a radiologist opens a study in PACS, the SaMD platform automatically retrieves the DICOM images, runs AI inference, and generates diagnostic recommendations with confidence scores. The explainability layer shows which imaging features contributed to each recommendation. The context engine surfaces relevant patient history, prior studies, and clinical notes. The radiologist reviews AI outputs alongside their own clinical judgment, documents findings, and finalizes the report. All AI outputs and clinician actions are logged for audit and continuous learning.

How the Platform Works

Platform Capabilities

01

AI-Powered Diagnostic Engine

Deep learning models for abnormality detection and diagnostic recommendations.

02

Explainability Layer

Feature attribution showing which imaging features contributed to recommendations.

03

DICOM-Aware PACS Integration

Query, retrieve, process, and route results without workflow disruption.

04

Clinical Context Aggregation

Patient history, prior studies, and clinical notes surfaced in workflow.

05

Low-Latency Inference

AI recommendations delivered within 30 seconds per study.

Measurable Clinical Impact

30%

improvement in diagnostic accuracy compared to pre-AI baseline.

40%

reduction in clinician cognitive load (self-reported).

50%

reduction in time spent cross-referencing patient history.

25%

reduction in missed findings during high-volume periods.

100K+

studies analyzed annually.

Technology Stack

  • AI/ML: Python with TensorFlow and PyTorch for deep learning models
  • Explainability: SHAP and Grad-CAM for feature attribution
  • ML Models:TensorFlow for VAE risk detection and predictive maintenance
  • DICOM Handling: pydicom and Orthanc for DICOM processing
  • Frontend: React.js for clinician dashboard
  • Backend: Node.js with RESTful APIs
  • PACS Integration:DICOM, HL7
  • EHR Integration: FHIR R4 APIs
  • Database: PostgreSQL for audit logs and metadata
  • Infrastructure: AWS (EC2, RDS, S3) with HIPAA-compliant hosting
  • Security:OAuth 2.0, TLS 1.3, AES-256 encryption
  • Compliance:FDA, IEC 62304, ISO 14971, HIPAA, GDPR
Technology
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What This Project Proves

SaMD-based clinical decision support can transform diagnostic imaging by improving accuracy, reducing cognitive load, and standardizing care across readers. By building an AI-powered SaMD platform with explainability, DICOM-aware PACS integration, and low-latency inference, we helped the imaging network improve diagnostic accuracy by 30% and reduce clinician cognitive load by 40%. The solution achieved regulatory compliance while creating a scalable foundation for AI-driven diagnostics.

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