Reading
This article introduces a RAG system specifically designed for thyroid cancer literature. Through evidence hierarchy stratification, confidence scoring, and traceability mechanisms, the system addresses the core challenges of answer credibility and verifiability in medical AI, providing a new technical paradigm for clinical decision support.
Section 01
This article introduces the open-source project Thyroid Cancer RAG Assistant. Through evidence hierarchy stratification, confidence scoring, and traceability mechanisms, the system addresses the core challenges of answer credibility and verifiability in medical AI, providing a new technical paradigm for clinical decision support.
Section 02
Medical scenarios have strict requirements for answer quality: Medical knowledge has a hierarchical structure (large differences in credibility among guidelines, reviews, trials, etc.), which ordinary RAG cannot distinguish; medical decision-making has extremely low fault tolerance and requires verifiable sources, but ordinary RAG lacks this mechanism, restricting clinical application.
Section 03
The underlying layer uses Qdrant vector database to store thyroid cancer literature with evidence hierarchy labels; retrieval supports filtering by evidence hierarchy; the all-MiniLM-L6-v2 model is used for vector conversion; generation uses OpenAI Responses API, with source citations attached to outputs; front-end uses Streamlit, vector database chooses Qdrant Cloud.
Section 04
The system classifies literature into seven levels (from clinical guidelines/expert consensus to case reports). The stratification directly affects retrieval strategies and confidence calculation: higher-level evidence has higher weight; if there is consensus among high-level evidence, confidence is high; if there is conflict, confidence is reduced and a prompt is given.
Section 05
Confidence integrates evidence hierarchy distribution, source consistency, and claim coverage; it provides a "credibility check" mode that can verify third-party medical claims and return whether they are supported by literature, the degree of support, and relevant evidence.
Section 06
Applicable scenarios: Doctors' outpatient quick queries for evidence-based answers with sources, researchers verifying viewpoints, medical education tools; note: For research and education use only, not medical advice.
Section 07
Insights: Domain knowledge integration is more important than general capabilities; verifiability is core; confidence quantification is key for clinical implementation. Limitations: Only covers thyroid cancer, literature lags, LLM may still have hallucinations. Future directions: Expand disease coverage, real-time literature updates, multimodal fusion, fine-grained confidence calibration.
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