Citation and Traceability for Medical Imaging Device

Data Characteristics for This Category

Registration documents for medical imaging devices typically include extensive technical documentation, test reports, clinical trial data, risk management files, and instruction manuals. These documents are often in PDF format, with complex structures containing numerous figures, formulas, and specialized terminology. Data sources primarily include internal manufacturer R&D documents, third-party testing agency reports, clinical institution research findings, and regulations and guidelines published by the National Medical Products Administration (NMPA). Data update frequency is relatively low, mainly occurring during product iterations, regulatory updates, or risk assessment cycles. Fields and units within documents have high standardization requirements, such as radiation dose units (mGy), resolution units (lp/mm), and signal-to-noise ratio (dB). This specialized data is often presented in tables or specific formats.

Constraints Imposed by These Characteristics on Citation and Traceability

The complex structure and specialized nature of medical imaging device registration documents impose specific requirements on citation and traceability. The presence of figures and formulas in documents means that simple text chunking might lose critical contextual information, affecting retrieval accuracy. Specialized terminology and standardized units require the knowledge base to effectively identify and associate this information, ensuring the completeness and accuracy of cited content. The low update frequency allows for a comprehensive initial indexing of the knowledge base, with subsequent incremental updates focused on regulatory changes or product modifications. Furthermore, the high demand for data accuracy necessitates that recalled citations precisely point to specific paragraphs or pages in the original document, facilitating quick verification by engineers.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`800–1200 characters`	Accounts for long sentences and complex paragraphs in medical imaging device documents, maintaining sufficient context while preventing overly large chunks from introducing irrelevant information.
`Chunk Overlap Length` (Overlap)	`150 characters`	Ensures contextual continuity at chunk boundaries, especially near figures or formulas, preventing critical information from being split.
`Recall count` (Recall Limit)	`Top 8–12 entries`	The rigor of registration documents requires covering as much relevant information as possible. Increasing the recall limit enhances comprehensiveness, with subsequent refinement through re-ranking.
`Similarity threshold` (Similarity Threshold)	`0.78–0.85`	Medical imaging device documents contain extensive specialized terminology and normative text. A high threshold helps exclude vague matches, ensuring the precision of recalled content.
`Rerank result count` (Rerank Limit)	`Top 3 entries`	After recall and re-ranking, focuses on the most relevant and high-quality citations, reducing the review burden for engineers and improving efficiency.
`maxContext`	`4000 token`	Ensures the AI model can process a sufficiently long context to understand complex specialized descriptions and regulatory requirements, supporting accurate citation generation.

Three Common Pitfalls

Citation results contain numerous irrelevant or duplicate regulatory provisions. This occurs because the Similarity threshold (Similarity Threshold) is set too low or the Chunk size (Chunk Size) is too short, leading to insufficient contextual information in the chunks and an inability to differentiate the specific applicability of regulatory provisions.
AI-generated declaration content has inaccurate or missing citation sources. This occurs because the knowledge base chunking failed to effectively process figures, formulas, or cross-page content in the documents, resulting in incomplete chunk content or loss of critical information.
The sorting logic of the citation merging module does not meet expectations after calling different knowledge bases in the workflow. This occurs because the Rerank result count (Rerank Limit) is set improperly or a unified sorting strategy for multi-knowledge base retrieval results is not configured.

How to Confirm Proper Configuration

Select typical questions from medical imaging device registration documents. Test the AI-generated answers and verify whether the citations accurately point to specific paragraphs or pages in the original document.
Check specialized terminology, units, and data in the AI-generated content. Confirm that they are fully consistent with the statements in the cited sources, without deviation or misinterpretation.
Simulate key regulatory updates or product technical parameter changes. Verify the knowledge base's incremental update mechanism and check the accuracy of citations for new and old versions of documents.
Evaluate the number and relevance of AI-recalled citations under varying query complexities. Ensure that Recall count (Recall Limit) and Similarity threshold (Similarity Threshold) balance comprehensiveness and precision.

Note: The values provided are common starting points. Measure against your own samples for optimal performance.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.