Workflow Orchestration for Dermatology Registration and

Data Characteristics in this Category

Dermatology registration and declaration documents involve diverse data types. These primarily include clinical trial reports, non-clinical study reports, manufacturing process specifications, quality standards, and stability study data. This data often exists in various formats such as PDF, Word, and Excel, containing numerous charts, images, and both structured and unstructured text. For example, clinical trial reports typically include patient enrollment information, medication records, and adverse event rates. This data has a low update frequency, primarily generated centrally after clinical trials conclude. Manufacturing process specification document updates relate to production batches and process changes, potentially involving more frequent, minor revisions. Specific fields require precise recording of indicators like lesion area (e.g., BSA percentage), lesion score (e.g., EASI index), and imaging characteristics (e.g., ultrasound probe frequency MHz), with units remaining relatively fixed.

Constraints Imposed by these Characteristics on Workflow Orchestration

The data characteristics of dermatology declaration documents impose specific requirements on workflow orchestration. First, the multimodal document formats (PDF, Word, Excel) necessitate the integration of various file parsers within the workflow to ensure effective extraction of text, tables, and image content. Second, the low update frequency of clinical data and manufacturing process documents means the workflow does not require frequent full updates during the data ingestion phase. It can rely more on incremental or version management mechanisms. The presence of specific fields like lesion area and lesion scores requires the workflow to possess highly customized entity recognition capabilities during information extraction. This involves, for instance, using regular expressions or custom dictionaries to identify indicators like BSA and EASI and extract their values, ensuring unit accuracy. Finally, charts and images within the documents may require the workflow to integrate image recognition (OCR) or chart parsing modules to convert visual information into searchable text.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale for this Value
`Chunk Size`	`500–800 characters`	Paragraphs in dermatology reports often contain complete concepts. Too short may break context, while too long increases recall noise.
`Recall Count`	`Top 5–7 items`	Declaration documents demand high information completeness. Appropriately increasing recall count improves relevance coverage.
`Similarity Threshold`	`0.75–0.85`	Ensures the precision of recalled content, preventing irrelevant or low-relevance information from interfering with declaration preparation.
`Rerank Return Count`	`3 items`	After reranking, select the most relevant few items to help engineers quickly focus on key information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Large clinical reports or multi-chart PDF files take longer to parse, requiring a longer timeout.
`maxContext`	`32000 tokens`	Ensures the model has sufficient contextual understanding when processing complex and varied dermatology clinical data.

Three Common Mistakes

Workflow saving appears successful, but refreshing the page shows content loss. This occurs due to backend cache synchronization delays or outdated frontend state. Try clearing browser cache or checking the FastGPT version.
Global variables are not effective during API calls. The model's response lacks key information or has an incorrect format. This typically happens when global variables are not correctly configured as Conversation-level Variables or Session-level Variables.
Document parsing fails, with logs showing Unsupported file type or Parsing timeout. This is because OCR or specialized table parsers are not enabled for common scanned PDFs or complex table images found in dermatology documents.

How to Verify Correct Configuration

Upload a PDF document containing key indicators like lesion area and lesion score. Verify if the workflow can accurately extract these fields and their values.
Call the workflow via API, passing custom Conversation-level Variables. Check if the model's response correctly references these variables.
Upload a Word document containing complex tables and charts. Examine the knowledge base chunking results to confirm if table content is effectively parsed and split.

The values provided are common starting points and should be measured against the reader's 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.