AI-powered stethoscope system for healthcare professionals
This AI-powered stethoscope system is designed to elevate healthcare diagnostics and foster collaboration among all users. With precise, AI-driven analysis, it provides essential insights for healthcare professionals, doctors and nurses alike, enabling more accurate and timely diagnoses.
Patients receive personalized care as their health data is securely captured and assessed. Meanwhile, system administrators ensure the technology operates seamlessly, adapting to diverse healthcare environments. This comprehensive healthcare solution enhances patient-centered care and boosts operational efficiency across medical settings.
Technical stack includes:
The history
The client aimed to create an AI-powered stethoscope to modernize diagnostics. This telehealth tool uses AI to analyze heart and lung sounds, enabling early disease detection. We began with a POC, showcasing the potential of AI in stethoscope data interpretation for accurate diagnostics.
The team
We started this project in 2021, with development in 2022, and resumed in 2024 after a two-year pause for FDA certification. Our team, including backend, frontend, mobile, DevOps, UI/UX, QA, data scientists, and project managers, collaborated with hardware experts to build an end-to-end solution.
Ardas role
We developed a software suite and AI model for the stethoscope device, including a mobile app for AI analysis, a web app for secure data management, and a scalable cloud backend. Our solution supports 10,000 users, ensuring high performance, reliability, and seamless integration with the hardware.
Challenges
Meeting FDA, HIPAA, GDPR, and HL7 FHIR standards was critical. Data security relied on anonymization and end-to-end encryption. We tackled noise filtering with advanced AI, achieving 85% diagnostic accuracy while ensuring scalability, 99.9% uptime, and offline functionality for remote areas.
Provided Solution
Mobile application for iOS and Android:
- Synchronizes sound data from the stethoscope device
- Provides access to AI-driven analysis results directly on mobile
- Supports offline functionality with synchronization once reconnected
- Conducts sound labeling and data management
Web application for data management and analysis:
- Designed for healthcare administrators and clinicians
- Offers a secure, user-friendly interface for reviewing and monitoring patient data
- Supports detailed, in-depth analysis for ongoing patient care
Product Improvements
AI Model for real-time sound analysis:
- Works in tandem with the device to classify and analyze heart and lung sounds in real time
- Ensures healthcare professionals receive precise, actionable insights quickly
Cloud-based backend services:
- Manages secure data storage and synchronization between mobile and web applications
- Ensures system scalability and performance, supporting up to 10,000 concurrent users
- Provides real-time data access and robust uptime for reliable healthcare delivery
Navigating Challenges for Results
Regulatory Compliance
Challenge: Ensuring adherence to FDA Class II certification, HIPAA, GDPR, and HL7 FHIR standards for healthcare data interoperability, to meet legal and market requirements.
- Result: Delivered a compliant system that achieved legal and market readiness.
Data Security
Challenge: Balancing robust data security with the client's preference for anonymized patient data over traditional end-to-end encryption and secure storage.
Result: Implemented a solution where only patient IDs were stored, with full names recorded separately by doctors, ensuring privacy without compromising security.
System Performance and Scalability
Challenge: Designing a system with rapid response times, 99.9% uptime, scalability to 10,000 simultaneous users, and offline functionality for areas with limited connectivity.
Result: Delivered a high-performing, scalable system adaptable to diverse healthcare environments.
Noise Filtering for Diagnostics
Challenge: Eliminating external interference from heartbeat recordings to ensure accurate diagnostics.
Result: Used advanced signal processing and machine learning to achieve noise reduction, meeting a target accuracy of over 85%.
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