Multilingual Virtual Healthcare Assistant.

Area of Science:

• Artificial Intelligence in Healthcare
• Natural Language Processing for Medical Applications
• Computational Linguistics

Background:

• Conventional healthcare assistants struggle with multilingual support and complex user inputs.
• Long Short-Term Memory (LSTM) models face challenges with long-range dependencies in sequential data.
• Need for advanced models capable of nuanced understanding in diverse linguistic contexts.

Purpose of the Study:

• To propose a virtual healthcare assistant framework leveraging transformer models.
• To enhance multilingual support for efficient and accurate healthcare assistance.
• To improve disease diagnosis and prediction using advanced AI.

Main Methods:

• Utilized a transformer model for processing sophisticated, multilingual user inputs.
• Employed self-attention mechanisms for parallel processing and contextual understanding.
• Compared transformer model performance against Long Short-Term Memory (LSTM) models.

Main Results:

• Transformer model achieved 85% accuracy, significantly outperforming LSTM's 65% accuracy.
• Demonstrated superior performance in multilingual translation (e.g., English to French: 0.7 BLEU).
• Achieved over 85% accuracy in disease prediction based on symptom analysis across demographics.

Conclusions:

• Transformer architecture offers significant advantages for multilingual virtual healthcare assistants.
• The proposed system provides accurate disease diagnosis and prediction with enhanced contextual understanding.
• The virtual assistant effectively supports users in their preferred language for improved healthcare access.