Leveraging multilingual RAG for breast cancer RCPs: AI-driven speech transcription and compliance in Darija-French clinical discussions

Abstract

• First end-to-end multilingual Voice-RAG for oncology RCPs (Darija/French) with real-time notes. • High-accuracy Darija ASR (BERTScore F1≈100 on DODa) with strong cross-corpus generalization. • Sentence-level retrieval (HNSW) plus compliance guardrails for toxicity, leakage, hallucination. • Reliable RAG across 13 LLMs with high answer relevance and groundedness on 40 clinical queries. Extensible to other dialects/specialties; next steps: clinical corpora and latency optimization. The integration of artificial intelligence (AI) into clinical decision-making has introduced new opportunities for automating and enhancing medical documentation, particularly in oncology, where multidisciplinary meetings are central to treatment planning. However, existing speech-to-text and retrieval-augmented generation (RAG) systems are not equipped to operate effectively in multilingual, dialect-rich environments such as those in North African hospitals where Moroccan Darija, Arabic, and French are frequently interwoven. These linguistic complexities, combined with the high-stakes nature of clinical dialogue, challenge transcription accuracy, contextual information retrieval, and regulatory compliance. This study presents a multilingual RAG system tailored to clinical meetings, integrating a fine-tuned Whisper ASR model with a sentence-level semantic retrieval pipeline and a compliance-aware generation framework. Evaluated on real-world clinical queries, the system demonstrates improved transcription quality and retrieval precision over standard pipelines, while enforcing factual grounding and safety through multi-stage output validation. These results highlight the potential of multilingual, speech-driven AI to support decision-making and compliance in linguistically diverse healthcare environments, offering a deployable foundation for clinical NLP in underserved regions.

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