Knowledge Base Retrieval and Recall for Medical Imaging

Data Characteristics for This Category

Data for medical imaging device clinical trial pre-screening primarily comes from product manuals, technical whitepapers, user guides, maintenance instructions, calibration reports, and regulatory compliance documents. Device manufacturers typically publish these documents. Updates align with product iteration cycles and regulatory changes, occurring quarterly or annually. Documents are often in PDF format, containing numerous charts, technical parameter tables, operational flowcharts, and diagnostic image examples. The text is highly specialized, filled with terminology from medical imaging, physics, and engineering. Fields and units are highly standardized, such as device model (GE Revolution CT), image resolution (512x512 pixels), radiation dose (mSv), scan time (seconds), and magnetic field strength (Tesla).

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

Medical imaging device documentation contains dense, specialized terminology and often mixes languages (Chinese and English). This demands strong text comprehension and multilingual processing capabilities from the knowledge base. Tables and diagrams in documents easily lose context during traditional text segmentation, affecting information completeness. The precision of technical parameters is crucial for pre-screening, for example, requirements for specific FOV (field of view) or kVp (tube voltage). Recall results must precisely match numerical ranges; simple keyword matching may not suffice. Furthermore, document version differences due to device updates require the knowledge base to distinguish and prioritize the latest or specified version information. Although document update frequency is not high, each update can involve critical performance parameter adjustments. This necessitates an efficient incremental update and version management mechanism in the knowledge base to ensure pre-screening always relies on the latest valid data.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk Size`	`800–1200 characters`	Medical imaging device document paragraphs are typically long, containing complete technical descriptions. Shorter chunks easily break context.
`Overlap Size`	`100 characters`	Ensures contextual continuity between adjacent chunks, handling technical descriptions that span paragraphs.
`Text Embedding Model`	`High-precision model`	Handles a large volume of specialized terminology and complex sentence structures, improving semantic understanding accuracy.
`Recall Count`	`Top 5–8`	Given the precision requirements for medical imaging device pre-screening, increasing the recall count covers more potentially relevant information.
`Similarity Threshold`	`0.75–0.85`	Strictly controls similarity to reduce the recall of irrelevant or low-quality information, ensuring pre-screening rigor.
`Rerank Count`	`Top 3`	After reranking, prioritize a small number of the most relevant results for quick engineer decision-making.

Three Common Pitfalls

After inputting a Chinese query, recall results do not include English documents from the knowledge base. This may be because the knowledge base lacks multilingual processing capabilities, or the text embedding model has insufficient semantic understanding for mixed Chinese and English queries.
Knowledge base files were segmented using newlines, but after setting custom delimiters, chunk lengths remain suboptimal, leading to paragraph merging or truncation. This usually occurs because the combination logic of Chunk Size and custom delimiters does not adequately consider the document's actual structure, or the segmentation algorithm's handling of consecutive newlines does not meet expectations.
Calling the model's built-in tools (e.g., web search) fails, with the interface indicating unavailability. This suggests the model's integration within the FastGPT platform may not have fully enabled its external tool calling capabilities, or it lacks the corresponding API_KEY or permission configurations.

How to Verify Configuration

For typical pre-screening scenarios (e.g., "Find MRI device compatibility with ferrous implants"), use mixed Chinese and English queries. Check if recall results include multilingual documents and evaluate if key technical parameters are accurately presented.
Upload a medical imaging device technical manual containing complex tables and captions. Observe the knowledge base chunk preview to confirm that table content and its associated text are reasonably segmented and indexed, without losing critical information or breaking context.
Simulate a query with specific numerical requirements (e.g., "What is the X-ray tube voltage range for CT devices?"). Verify that the kVp values mentioned in the recall results match the data range in the original document and that units are correct.
After updating a new version of a technical whitepaper for a medical imaging device in the knowledge base, re-query for that device's information. Verify that recall results prioritize the new version's content and accurately identify version differences.

Note: The values provided are common starting points. Measure them 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.