Development of a GI Cancer Diagnostic Chatbot Based on RAG Model

Abstract

Gastrointestinal (GI) cancer involves the digestive tract and is not easy to detect because it comes with vague signs and intricate medical reports. The present study puts forward a new use of RoBERTa large, a transformer language model, and Retrieval-Augmented Generation (RAG). Our model exhibits important advancements with three innovations: Domain-specific embeddings employing medically fine-tuned RoBERTa (Robustly Optimized BERT Pretraining Approach), dynamic knowledge incorporation of latest research through RAG (Retrieval-Augmented Generation) architecture, and hybrid ranking leveraging semantic and keyword-based relevance. Experimental results establish 51% cosine similarity in test set responses with retaining clinical coherence.

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