Workflow Orchestration for Clinical Trial Pre-screening of

Data Characteristics for This Category

Data generated by medical imaging devices during clinical trial pre-screening primarily includes medical images (e.g., CT, MRI, X-ray), imaging reports, and device-generated metadata. Medical images are predominantly in DICOM (Digital Imaging and Communications in Medicine) format, containing image data and rich patient, examination, and device information. Imaging reports are typically unstructured text, describing imaging findings and diagnostic conclusions; these may use various templates and free-form text. Device metadata includes scanning parameters, reconstruction algorithm versions, and sequence information, usually embedded in DICOM file headers. Data sources include hospital PACS systems or local device storage. Update frequency depends on the clinical trial protocol and patient examination schedule, potentially ranging from multiple times daily to on-demand updates.

Constraints Imposed by These Characteristics on "Workflow Orchestration"

The complexity and diversity of DICOM data necessitate robust file parsing and metadata extraction capabilities within the workflow to filter patients based on specific imaging features. Unstructured imaging report text requires integrating Natural Language Processing (NLP) modules to identify key medical terms, disease manifestations, or measurement results from free-form text. This increases the frequency of AI model calls and the demand for text processing plugins within the workflow. The non-periodic nature of data updates means that workflow triggers cannot rely solely on fixed time points; event-driven mechanisms are necessary, such as new image file uploads or PACS system notifications. Furthermore, imaging files are often large, requiring the workflow to consider network bandwidth and storage capacity during transfer and storage. This impacts the concurrent processing capabilities and timeout settings of file processing plugins.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`UPLOAD_FILE_MAX_SIZE`	`2000 MB`	Individual DICOM series files can be large; ensure complete upload.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	DICOM file parsing involves image decompression and metadata extraction, which can be time-consuming.
`maxContext`	`2000 characters`	Imaging report text is often long; sufficient context is needed for NLP model analysis.
`Recall count`	`Top 20 entries`	Increase recall during initial screening to avoid missing potentially eligible patients.
`Similarity threshold`	`0.75`	Strictly match clinical trial enrollment criteria to reduce false positives.
`Rerank result count`	`Top 5 entries`	After re-ranking, focus on the most relevant patient information for manual review.

Three Common Pitfalls

Custom plugins in workflow tasks do not display input and output parameters. This typically manifests as an empty parameter list in the plugin configuration interface. The cause is incorrect declaration or formatting of the inputs or outputs fields in the plugin definition file plugin.json.
The workflow fails to obtain user-uploaded image file parameters. This manifests as the HTTP request module receiving an empty or invalid file path parameter. The cause is that the file upload module does not correctly pass the file handle or an accessible URL to subsequent workflow steps.
The loop execution module fails to call AI sessions or processes only some elements when handling an array<string> type array. This manifests as AI calls within the loop body returning errors or incomplete results. The cause is concurrency limits on AI calls within the loop body or the request body size of a single AI session exceeding limits.

How to Verify Correct Configuration

After uploading a DICOM file to the platform, verify that the file size and format are correctly recognized via file management or debug logs.
Execute a workflow that includes DICOM parsing and report text extraction. Check that the intermediate step outputs contain complete DICOM metadata and structured key information from the report.
Run a pre-screening workflow. Input a set of imaging data known to meet and not meet pre-screening criteria. Check that the workflow's final output patient list is accurately categorized.
Check workflow execution logs to confirm that all custom plugins and AI modules show a successful call status, with no timeout or resource limit-related error codes.

The values given 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.