Data Characteristics for This Category
Rehabilitation device registration data comes from various sources. These include medical device classification catalogs, registration guidelines, and technical review reports published by the National Medical Products Administration (NMPA). International standards like ISO 13485 and the IEC 60601 series are also sources. Internal company documents, such as R&D documentation, test reports, clinical evaluation reports, and risk management reports, contribute as well. Update frequencies vary. Regulatory documents typically update annually or through special revisions. Internal company documents update with product R&D iterations. Document structures are often hierarchical Word, PDF, or XML formats. They contain extensive technical parameters, performance indicators, test data, and charts. Fields and units are critical. For example, performance indicators involve mechanical units (Newton, Pascal), electrical units (Volt, Ampere), time units (second, minute), and angular units (degree). Strict requirements apply to precision and test conditions.
Constraints from These Characteristics on Multi-turn Conversations and Prompts
The large volume and diverse sources of rehabilitation device registration data require multi-turn conversation systems to have efficient information retrieval and integration capabilities. The hierarchical document structure and specialized terminology necessitate precise prompt design. This guides the model to accurately understand user intent and locate key information. For instance, when a user asks about a rehabilitation robot's "maximum load capacity," the system must identify the corresponding technical parameter. It must also extract specific values and test standards from test reports. The update frequency of regulatory documents requires the system to synchronize with the latest policies. This prevents citing outdated information. Differences in fields and units across various document formats challenge model parsing and data extraction. Prompts must guide the model to standardize this heterogeneous data. This ensures accuracy and consistency in responses. In multi-turn conversations, users may progressively refine questions. For example, from "registration requirements for knee rehabilitation devices" to "their biocompatibility testing standards." This requires the model to maintain context and perform deep information mining based on prior conversations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Rehabilitation device registration data has strong contextual relevance; a longer conversation history is needed. |
Chunk size (Segment Length) | 500 characters (characters) | Ensures individual segments contain sufficient technical details, adapting to the density of regulatory and technical documents. |
Recall count (Recall Count) | Top 10 entries (top 10) | Increases the probability of retrieving relevant technical specifications or test reports from a large volume of documents. |
Similarity threshold (Similarity Threshold) | 0.78 | Balances recall and accuracy, reducing interference from irrelevant information and focusing on professional content. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5) | Further optimizes retrieval results, prioritizing the most relevant regulatory clauses or technical parameters. |
systemPrompt | Calibrate based on actual measurements | Must include role-specific terms like "medical device regulatory expert" and "rehabilitation device registration and declaration," emphasizing accuracy and compliance. |
Three Common Mistakes
- A dialog response states, "Cannot find regulations for this standard number." This may occur because the knowledge base has not been updated with the latest medical device regulations or international standards, preventing the model from matching.
- The model confuses data units when answering equipment performance indicators, for example, incorrectly using "N" and "kg." This happens because the prompt did not explicitly require the model to monitor and verify data unit consistency.
- After a user's question, the model's response is too broad and fails to focus on specific technical details of rehabilitation devices. This is due to insufficient semantic constraints on "rehabilitation devices" in the prompt, which did not effectively differentiate them from general medical devices.
How to Confirm Proper Configuration
- Input a specific rehabilitation device's registration certificate number or product model. Verify that the system accurately recalls key documents, such as its registration information and product technical requirements.
- Ask about a key performance parameter for a rehabilitation device (e.g., "maximum output force," "battery life"). Check if the values provided in the model's answer match the documented records and if the units are correct.
- Simulate a user's question about a regulatory clause, such as "What are the electrical safety standards applicable to XX-type rehabilitation robots?" Check if the model cites the latest IEC 60601 series standards and provides specific clauses.
Note: The values provided are common starting points. Measure them against your own samples.
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.