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In an era defined by convenience and sustainability, Santosh Diagnostics emerges as a beacon of innovation in the Indian healthcare landscape. By bringing a plethora of diagnostic tests directly to patients' homes, Santosh Diagnostics revolutionizes the traditional healthcare experience. Through advanced digital processes, particularly in X-ray and ECG services, the company not only enhances accessibility but also champions environmental responsibility, saving millions in X-ray film costs. To achieve this goal, Santosh Diagnostics partnered with Minfy to leverage a suite of AWS Services in conjunction with their in-house solution Healthstorz.
Healthcare providers deal with a large volume of medical imaging data, including X-rays, CT scans, MRI scans, and ultrasound images, which are time-consuming to analyze and interpret due to the specialized expertise required. The shortage of skilled radiologists, combined with the increasing demand for medical imaging services, exacerbates this challenge. Imaging reports play a vital role in communicating findings to other healthcare providers and ensuring proper patient care. However, delays in diagnosis and treatment due to this shortage can potentially impact patient outcomes. Additionally, manually creating reports is time-consuming, labor-intensive, and prone to errors.
Minfy recommended a tailored AWS solution to address Santosh Diagnostics challenges:
GenAI's advanced medical imaging and summarization service, integrated to provide AI-powered analysis, annotation, and generation of comprehensive summaries for the medical images.
Leveraged as a serverless compute service to perform data processing tasks on the medical imaging data, such as image preprocessing, anonymization, and invoking the Bedrock endpoint for summary generation.
Utilized as a highly scalable and durable object storage service to securely store and manage the large volumes of medical imaging data (X-rays, CT scans, MRI scans, ultrasound images).
Employed to create, publish, and manage a secure and scalable API, enabling healthcare providers and patients to seamlessly upload medical images and retrieve analysis results.
Chosen for its scalability and low-latency performance, DynamoDB became the primary database for storing data.
Minfy executed a comprehensive Generative AI solution that leverages advanced machine learning models and computer vision techniques to automate the process of scanning and summarizing medical images. GenAI's summarization models provide concise and human-readable summaries that highlight the key findings and insights. These summaries provide a comprehensive overview of the image analysis, including descriptions of any detected abnormalities, potential diagnoses, and recommendations for further investigation or treatment. The summaries generated by GenAI are designed to be easily understandable by healthcare professionals, enabling them to quickly grasp the essential information. The solution consists of the following components:
Amazon S3: Medical imaging data (e.g., X-rays, CT scans, MRI scans, ultrasound images) is securely stored in an Amazon S3 bucket.
AWS Lambda: An AWS Lambda function is triggered when new imaging data is uploaded to the S3 bucket. This function performs necessary preprocessing tasks, such as image resizing and format conversion.
API Gateway: Employed to create, publish, and manage a secure and scalable API, enabling healthcare providers and patients to seamlessly upload medical images.
Amazon Bedrock: A state-of-the-art multimodal AI model, Bedrock Claude 3 Opus, is employed for analysing and interpreting medical images. This model combines advanced computer vision techniques with natural language processing capabilities, enabling it to identify potential abnormalities, lesions, or other relevant findings in medical images and generate detailed descriptions and potential diagnoses.
AWS Lambda: Another AWS Lambda function is triggered to invoke the Bedrock endpoint for image analysis. This function receives the imaging data, sends it to the respective model, and compiles the results into a comprehensive report.
API Gateway: A scalable API retrieves analysis results for the uploaded medical images and delivers it to the user.
Amazon DynamoDB: Chosen for its scalability and low-latency performance, DynamoDB became the primary database for storing the analysis results and associated metadata.
Analytics Services: S3
Rationale: Highly scalable and durable object storage for storing medical images.
Analytics Services: API Gateway
Rationale: Fully managed service to create, publish, maintain, monitor, and secure APIs at any scale.
Analytics Services: Lambda
Rationale: Serverless compute service used for data processing tasks on the image data and invoking the Bedrock endpoint for summary generation.
Analytics Services: Bedrock
Rationale: Service for medical imaging and summarization. Utilizing Anthropic Claude 3 Opus for image processing.
Analytics Services: DynamoDB
Rationale: Scalable and high-performance database for storing analysis results and related metadata.
1. Improved Efficiency: The GenAI solution reduces the time required for medical image analysis and report generation by over 40%, and effort required by 25%, enabling healthcare providers to focus more on patient care.
2. Scalability and Accessibility: The cloud-based architecture allows for seamless scalability, enabling the solution to handle 1000s medical imaging data and accommodate growing demand.
Enhanced Patient Outcomes: Faster analysis and reporting of medical images lead to 20% earlier diagnosis and treatment, potentially improving
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