Virtual Health Assistant in Telemedicine with INTEL Gaudi 3
Harnessing Intel Gaudi 3 to revolutionize telemedicine with faster AI, smarter diagnostics, and scalable care.
Patients are often looking for medical advice or information about their health, symptoms, or medication without having to wait for an appointment with a doctor. Current systems rely heavily on static chatbots that provide basic advice or FAQs, but there is a gap in delivering personalized, detailed medical guidance in real-time, especially during non-urgent hours.
Using INTEL Gaudi 3 along with Elasticsearch Vector database, a Virtual Health Assistant can be built that combines deep learning, natural language processing (NLP), and integration with medical databases to assist in telemedicine.
Key Benefits
- 24/7 Availability: Patients can get health-related support and advice at any time, reducing the need for waiting hours or days to see a healthcare provider.
- Efficiency: Medical professionals can focus on high-priority cases while the Virtual Health Assistant handles routine inquiries, symptoms, and follow-ups.
- Personalization: The Virtual Health Assistant becomes more intelligent and personalized with each interaction, offering tailored health advice based on historical data and real-time inputs.
- Scalability: Gaudi 3’s performance allows the system to handle a large number of simultaneous interactions without compromising speed or quality, making it suitable for large-scale healthcare platforms.
- Data-Driven Decision Making: Real-time integration with medical databases and the AI’s ability to analyze a broad range of data results in more informed, accurate responses.
- Symptom Analysis
User Role(s): Patient
The patient interacts with the Virtual Health Assistant describing their symptoms in step 1. For example, “I feel dizzy when I stand up" or "I've been feeling fatigued for the past week."
The patient input is then processed by the RAG enabled application in step 2. Relying on the Elasticsearch vector database, the patient input is used to perform symptom classification by matching them with existing medical records and patterns in step 3. This allows for improving the context. The improved context is then fed into the generative model in step 4. This helps the model craft more accurate, contextually appropriate, and informative responses in step 5.
Intel Gaudi 3 uses advanced NLP models to analyze the context, tone, and intent of the user's input, which allows it to understand complex symptoms.
- Personalized Health Recommendations
User Role(s): Patient, Data science engineer
Once the symptoms are identified, the RAG enabled application suggests potential conditions or medical advice in step 6 and 7. Those conditions or recommendations are based on its machine learning models trained on both structured data provided by Electronic Health Record and Medical database as well as unstructured data (complex pdfs, images, video). Data ingestion and training is handled by a Datascience engineer.
For example:
- “It seems like you may be experiencing orthostatic hypotension, which is when you feel lightheaded or dizzy when standing up too quickly.”
- The assistant could then recommend general home remedies, lifestyle changes, or direct the patient to appropriate specialists, leveraging Gaudi 3's contextual learning and data processing power.
- Real-Time Interaction with Medical Databases
User Role(s): NA
Intel Gaudi 3 can quickly interact with real-time medical databases or an electronic health record (EHR) system to pull the patient’s medical history or specific medication information. For example, if the patient reports being on certain medication, the Virtual Health Assistant can instantly check for drug interactions or potential side effects, offering instant insights:
"I see you're on medication X. There might be a mild interaction with Y, which could cause dizziness. Would you like to talk to a doctor about it?"
- Decision Support for Healthcare Providers
User Role(s): NA
If the symptom analysis suggests an urgent condition, the Virtual Health Assistant can immediately recommend the patient to consult a healthcare provider and can schedule virtual consultations. It could even generate a report with symptom analysis for the physician.
- Learning and Adaptation
User Role(s): NA
As more patients interact with the Virtual Health Assistant, Intel Gaudi 3 can continuously learn from those conversations, adapting its responses and refining its recommendations. It could adjust its suggestions based on the patient’s medical history and health trends, providing a more personalized experience over time.
