AI-Powered Lung Disease Detection for a Healthcare Provider in India
A deep learning system for automated lung disease detection from chest X-rays — improving diagnostic accuracy and speed for a healthcare provider across India.
60%
Faster DICOM scan analysis
95%+
Detection accuracy
HIPAA
Compliant data handling
Millions
Of scans supported annually
About the Client
A prominent Indian healthcare organisation operating multiple clinics and diagnostic centres needed to improve diagnosis accuracy, reduce manual workload, and maintain regulatory compliance while managing substantial volumes of patient imaging data. Radiologists were managing thousands of DICOM files daily, heavy reliance on manual interpretation led to delays in lung disease detection, and HIPAA-compliant data handling was a non-negotiable requirement across all sites.
The Challenge
Large Volumes of Medical Imaging
Radiologists faced difficulties managing and interpreting thousands of DICOM files daily — a volume that was unsustainable without intelligent automation.
Manual Diagnostics & Delays
Heavy reliance on manual image interpretation led to significant delays in lung disease detection and subsequent treatment planning.
Accuracy & Compliance Requirements
Ensuring precision in diagnosis while maintaining HIPAA compliance and Indian health data regulatory requirements was a critical, non-negotiable constraint.
What We Did
1Data Research & Model Development
- Conducted research on lung disease datasets specific to Indian patient demographics
- Partnered with radiologists to validate training data and annotation quality
- Ensured data compliance with HIPAA and Indian health data laws throughout
- Applied preprocessing techniques to improve image clarity and annotation accuracy
2DICOM File Processing & System Integration
- Built a pipeline to parse, process, and analyse DICOM files at scale
- Integrated with existing PACS systems for seamless image retrieval and storage
- Automated anonymisation to ensure patient data security and compliance
- Implemented metadata tagging for quicker case retrieval and audit readiness
3Workflow Optimisation & Scalability
- Designed APIs for real-time integration with hospital management systems
- Reduced reporting delays by auto-generating diagnostic summaries from DICOM scans
- Implemented scalability features to expand across multiple diagnostic centres
- Provided radiologists with AI-driven second opinions to minimise oversight risks
The Results
60% Faster Diagnosis
Reduced time to analyse DICOM scans by 60%, enabling quicker treatment decisions and better patient outcomes.
Improved Detection Accuracy
Enhanced detection rates for lung diseases with significantly lower false negative rates than manual review.
Full HIPAA Compliance
Ensured HIPAA-compliant storage, processing, and transfer of sensitive medical imaging data across all sites.
Streamlined Radiologist Workflows
Automated integration with hospital PACS eliminated manual image handling steps, freeing radiologists for complex cases.
Built for Scale
The system is designed to support millions of DICOM scans annually without performance degradation as the provider expands.
