Objective: To enhance diagnostic accuracy by dynamically suggesting possible ailments and remedies based on a patient’s symptoms and historical medical records.
Data Utilization:
- Patient Symptoms and History: Detailed analysis of historical patient data, including previous diagnoses and prescriptions.
- Treatment Effectiveness Insights: Leveraged historical treatment outcomes to improve future diagnostic accuracy.
Solution Design:
- AI Model Configuration:
- Model Selection: Utilized GPT-4-turbo for its contextual understanding and ability to correlate historical data with current symptoms, outperforming other models in interpretability and accuracy.
- Data Processing: Implemented Azure OpenAI for model hosting and real-time access to historical records via API integration.
- Prompt Optimization: Tailored prompts to evaluate symptom progression and historical treatment effectiveness.
- Prompting Method: Employed sequential prompting to refine diagnoses by progressively analyzing symptoms and historical data.
- Testing Methodology: Used confidence ratings to quantify the model’s suggestion reliability, thereby enhancing doctor trust in AI-assisted diagnosis.
Technical Stack:
- Cloud Infrastructure: Azure AI, Azure Blob Storage, and Azure Machine Learning for scalable storage and processing.
- Data Pipelines: Azure Data Factory and Azure Synapse Analytics for automated data ingestion and preprocessing.
- Vector Database: Pinecone for efficient retrieval and similarity search within historical patient records.
- Front-End Interface: React.js-based UI for doctors to interact with AI-generated diagnosis insights.
- Security & Compliance: HIPAA and GDPR-compliant encryption, access control, and multi-factor authentication.
- AI Monitoring & Governance: Azure Monitor, MLflow, and TensorBoard for model tracking and optimization.
Outcome:
- Provided dynamic suggestions for possible ailments and remedies based on individual patient histories, leading to more accurate and personalized medical care.
- Assisted doctors in understanding patient health patterns, contributing to improved patient outcomes and optimized prescription practices.
- Increased diagnostic confidence through AI-assisted historical data analysis, reducing the risk of misdiagnoses and improving treatment effectiveness.
This use case showcases how AI and historical data analytics can empower homeopathic practitioners with deeper insights, enhancing the accuracy and personalization of diagnoses and prescriptions.
