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Healthcare

Accelerating Medical Image Processing and Diagnoses for a Healthcare Provider Using Machine Learning

Clinician reviewing AI-analyzed medical images with annotations highlighting potential abnormalities for faster diagnosis.

Focus Areas

Medical Imaging

Machine Learning & Computer Vision

Clinical Workflow Automation

Computer screen displaying a deep learning algorithm processing MRI and CT scan images in real time.

Business Problem

A large healthcare provider faced increasing diagnostic backlogs due to rising patient volumes and limited radiology resources. Radiologists were overwhelmed by the manual analysis of high-resolution medical images (X-rays, CT scans, MRIs), leading to delays in diagnosis and treatment initiation. The provider sought to improve turnaround time for image interpretation without compromising diagnostic accuracy.

Key challenges:

  • Image Volume Overload: Daily imaging scans exceeded processing capacity, resulting in reporting delays.

  • Manual Review Bottlenecks: Radiologists were burdened with repetitive visual tasks, increasing fatigue and risk of oversight.

  • Inconsistent Accuracy: Variability in interpretation across facilities impacted diagnostic reliability

  • Lack of Integration: Existing PACS systems did not support intelligent image triage or real-time insights.

The Approach

Curate designed and deployed a machine learning-based image analysis pipeline that automated the triage, classification, and flagging of medical images for potential anomalies. Integrated within radiology workflows, this solution significantly reduced review times while enhancing diagnostic consistency and scalability.

Key components of the solution:

  • Discovery and Requirements Gathering: Curate partnered with the healthcare provider’s radiology, IT, and compliance teams to:

    • Identify key image modalities (e.g., chest X-rays, MRIs) for automation

    • Define diagnostic categories (e.g., tumor detection, pneumonia signs)

    • Establish performance benchmarks (precision, recall, latency)

    • Ensure compliance with HIPAA and FDA regulatory frameworks

  • Model Development and System Integration:

    • Data Preparation: Curated a dataset of over 1 million de-identified imaging scans, annotated by expert radiologists.

    • Model Training: Built convolutional neural networks (CNNs) using transfer learning and custom architectures for modality-specific tasks.

    • Use Cases: Included early detection of lung abnormalities, bone fractures, and brain lesions.

    • Performance Optimization: Models achieved over 92% precision and recall across key diagnostic targets.

    • Workflow Integration: Deployed via REST APIs into the provider’s PACS and EHR systems to surface alerts and classification labels directly to clinicians.

  • Automation and Quality Assurance:

    • Auto-Triage: Flagged high-risk images (e.g., suspected tumors, fractures) for prioritized human review.

    • Heatmap Visuals: Used Grad-CAM overlays to provide visual explanations of anomaly zones, aiding radiologist interpretation.

    • Continuous Learning: Enabled incremental retraining with newly reviewed images to improve model robustness.

    • Redundancy Checks: Maintained manual review protocols for flagged scans to ensure quality assurance.

  • Stakeholder Engagement & Change Management:

    • Radiologist Collaboration: Involved radiologists in validation, feedback, and refinement of model predictions.

    • Compliance Oversight: Ensured all image handling and storage adhered to HIPAA and FDA guidelines.

    • Training & Adoption: Delivered workshops on AI-assisted workflows and interpretation of AI-generated outputs.

    • Phased Rollout: Initiated with select facilities and scaled following positive results and clinician acceptance.

Business Outcomes

Faster Diagnostic Turnaround


Automated triage and classification reduced image interpretation time by over 50%.

Enhanced Diagnostic Accuracy


Assisted diagnoses led to improved anomaly detection consistency, especially for rare and subtle indicators.

Scalable Imaging Workflows


Enabled radiologists to focus on complex cases while automation handled routine image triage and classification.

Sample KPIs

Here’s a quick summary of the kinds of KPI’s and goals teams were working towards**:

Metric Before After Improvement
Average diagnosis time per image 12–15 min 6–8 min 45% faster
Image backlog (per week) 3,500 scans 1,000 scans 70% reduction
Detection accuracy (target conditions) 84–86% 92–94% 8% accuracy improvement
Radiologist satisfaction (survey score) 6.1/10 8.7/10 Increased efficiency rating
Compliance & audit error rates 3.2% 1% Improved quality assurance
**Disclaimer: The set of KPI’s are for illustration only and do not reference any specific client data or actual results – they have been modified and anonymized to protect confidentiality and avoid disclosing client data.

Customer Value

Faster Diagnoses


Reduced time-to-treatment for critical patients, improving health outcomes.

AI Transparency


Visual outputs built trust in model reliability.

Sample Skills of Resources

  • Computer Vision Engineers: Developed and fine-tuned CNNs for medical imaging analysis.

  • Radiology Consultants: Provided expert annotations and feedback for model training.

  • DevOps Engineers: Built secure, scalable pipelines for model deployment and monitoring.

  • Data Privacy Specialists: Ensured end-to-end data anonymization and compliance.

  • Project Managers: Oversaw stakeholder alignment, pilot execution, and change management.

Tools & Technologies

  • Machine Learning & CV: TensorFlow, PyTorch, OpenCV, Keras

  • Image Management: DICOM, PACS integration

  • Data Processing: Python, NumPy, Pandas

  • Deployment & APIs: Docker, REST, FastAPI

  • Visualization: Grad-CAM, Streamlit dashboards

  • Security & Compliance: HIPAA-compliant AWS infrastructure, FDA 21 CFR Part 11 alignment

Visualization of a machine learning system automatically categorizing X-ray images based on diagnostic features.

Conclusion

By integrating machine learning into medical image workflows, Curate helped the healthcare provider transform diagnostic operations—reducing turnaround times, enhancing accuracy, and enabling scalable, AI-assisted care. The project demonstrated how targeted automation, grounded in clinical best practices and robust ML models, can elevate the speed and precision of modern healthcare delivery.

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