feat(tests/fixtures): Add radiology AI anomaly detection example

[Hitendrasinhdata7]

This pull request adds a new, working example demonstrating how to use CQL for AI-assisted clinical decision support in radiology. The example provides a practical template for integrating clinical logic with AI workflows, specifically showing how to flag radiology findings for urgent review.

Changes
radiology_anomaly_detection.cql: A CQL library that defines logic to retrieve finalized radiology reports (DiagnosticReport) and flag those containing a high-risk conclusion code (SNOMED CT code 386661006).

radiology_anomaly_test.go: A Go example test that loads the CQL library, parses it with the FHIR 4.0.1 data model, and evaluates it against a sample patient bundle.

radiology_sample_bundle.json: A sample FHIR bundle containing a patient with one normal and one anomalous radiology report to validate the CQL logic.

Purpose / Motivation
This example serves as a clear, runnable template for developers looking to build systems where CQL clinical logic is used to identify cases that should be prioritized for AI-assisted review or other downstream processes.

Testing Performed
The included test passes: go test -v -run Example_radiologyAnomalyDetection.

The logic correctly identifies the report with the target SNOMED CT code and returns the decision "Prioritize for Urgent AI-Assisted Review".

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