Tool Calling and Plugins for Imaging Equipment

Data Characteristics in This Category

Imaging equipment data in pharmacovigilance primarily originates from Electronic Medical Record (EMR) systems, Radiology Information Systems (RIS/PACS), and device manufacturer maintenance logs. This data typically combines unstructured text (e.g., imaging diagnostic reports, handwritten doctor's notes), semi-structured data (e.g., DICOM image metadata, device alarm logs), and structured data (e.g., patient basic information, examination times, device models). Data updates frequently, with new imaging reports and device events generated almost in real-time. Diagnostic reports often contain free-text descriptions, including anatomical sites, abnormal findings, measurement data, and diagnostic conclusions. Device logs record operational status, error codes, and maintenance history. Fields and units vary; diagnostic reports might include "lesion size 2.5 cm" or "CT value 40 HU," while device logs record "temperature 37.5 ℃" or "current 2.1 A." Diverse and non-standardized unit expressions are common.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The highly heterogeneous nature of imaging equipment data requires tool calling and plugins to have robust multi-modal data processing capabilities. Parsing unstructured diagnostic reports necessitates Natural Language Processing (NLP) plugins to extract key medical entities and events, such as specific adverse event descriptions related to the device. Device information and examination parameters within semi-structured DICOM metadata require custom parser plugins for extraction. Error codes and operating parameters in device logs need standardization through regular expression matching or specific log parsing plugins. Due to the rapid data update frequency, plugins must support real-time or near real-time data ingestion and processing to avoid timeliness issues. Furthermore, the diversity of fields and units requires tool calls to perform unit conversion or standardization when passing parameters, for example, unifying size units from different reports to millimeters, ensuring subsequent analysis accuracy. Filtering for specific device models or serial numbers requires precise matching and filtering capabilities from tool calls.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`tool_timeout_seconds`	`60` seconds	Imaging report parsing or complex device log processing can be time-consuming; avoid premature timeouts.
`max_tokens_output`	`2048`	Ensures complete output of longer imaging report analysis results or detailed device event descriptions.
`extractor_regex_pattern`	Calibrate based on actual measurements	Precisely extract error codes and key parameters for different device log formats or report templates.
`nlp_entity_types`	`Device Model, Abnormal Site, Measurement Value, Event Description`	Ensures extraction of core pharmacovigilance-related information from free text.
`webhook_payload_format`	`JSON`	Standard industry data exchange format, facilitating integration with external systems and subsequent processing.
`max_retries`	`3`	Accounts for occasional network fluctuations or service interruptions in external systems (e.g., EMR, PACS), increasing call robustness.

Three Common Pitfalls

Tool calls return a 400 InternalError.Algo.InvalidParameter: messages with role \"to" error. This typically occurs when the recipient parameter to field for an email sending plugin is empty or incorrectly formatted, failing to retrieve a valid email address from the parsed data.
After project startup, even if MongoDB visualization tools connect successfully, the application still reports database connection errors. This might be due to the connection string or authentication information used by the application layer differing from that of the visualization tool, or the application not being correctly configured with the MongoDB driver.
The results returned by an HTTP API call are not correctly analyzed by the AI and do not output the required information. This could be because the HTTP response body structure is complex, and the plugin did not effectively clean and structure the data. This leads to the knowledge base ingesting raw, unstructured text, making it difficult for the AI to extract key facts.

How to Confirm Correct Configuration

Review tool call logs to ensure execution completes within tool_timeout_seconds. Check that the returned results contain expected key fields and data, such as device model, diagnostic conclusions, or adverse event descriptions.
Simulate actual data input to verify that extractor_regex_pattern accurately matches and extracts lesion sizes (e.g., 2.5 cm) from imaging reports or error codes (e.g., E101) from device logs. Also, check if units are standardized.
Perform end-to-end testing, from receiving raw imaging reports or device logs to the AI generating the final risk assessment or event report. Check that data flow and processing in all intermediate steps meet expectations, especially that entities extracted by nlp_entity_types are complete and accurate.

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.