Salvato in:
| Autori principali: | , , |
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| Natura: | Recurso digital |
| Lingua: | |
| Pubblicazione: |
Zenodo
2026
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| Soggetti: | |
| Accesso online: | https://doi.org/10.5281/zenodo.19554786 |
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Sommario:
- This research presents MedEase, an AI-powered medical chatbot designed to provide accurate, context-aware health guidance to users. The system integrates large language models (LLMs) with LangChain to manage conversational flow and maintain contextual understanding. To retrieve relevant medical information efficiently, Pinecone is employed for vector-based semantic search. A Flask web interface enables real-time user interaction, while AWS ensures scalable hosting, secure data management, and high availability. The chatbot has been evaluated on curated medical queries, demonstrating its ability to deliver reliable, understandable, and personalized responses. This work highlights how combining LLMs, vector databases, web frameworks, and cloud infrastructure can produce a practical and intelligent healthcare assistant, bridging gaps in accessible medical information.