Azure Cognitive Services Health Insights Radiology Insights client library for Python

Health Insights is an Azure Applied AI Service built with the Azure Cognitive Services Framework, that leverages multiple Cognitive Services, Healthcare API services and other Azure resources.

Radiology Insights is a model that aims to provide quality checks as feedback on errors and inconsistencies (mismatches) and ensures critical findings are identified and communicated using the full context of the report. Follow-up recommendations and clinical findings with measurements (sizes) documented by the radiologist are also identified.

Getting started

Prequisites

• Python 3.8 or later is required to use this package.
• You need an Azure subscription to use this package.
• An existing Cognitive Services Health Insights instance.

For more information about creating the resource or how to get the location and sku information see here.

Installing the module

python -m pip install azure-healthinsights-radiologyinsights

This table shows the relationship between SDK versions and supported API versions of the service:

Authenticate the client

Get the endpoint

You can find the endpoint for your Health Insights service resource using the Azure Portal or Azure CLI

# Get the endpoint for the Health Insights service resource
az cognitiveservices account show --name "resource-name" --resource-group "resource-group-name" --query "properties.endpoint"

Get the API Key

You can get the API Key from the Health Insights service resource in the Azure Portal. Alternatively, you can use Azure CLI snippet below to get the API key of your resource.

az cognitiveservices account keys list --resource-group <your-resource-group-name> --name <your-resource-name>

Create a RadiologyInsightsClient with an API Key Credential

Once you have the value for the API key, you can pass it as a string into an instance of AzureKeyCredential. Use the key as the credential parameter to authenticate the client:

import os
from azure.core.credentials import AzureKeyCredential
from azure.healthinsights.radiologyinsights import RadiologyInsightsClient

KEY = os.environ["AZURE_HEALTH_INSIGHTS_API_KEY"]
ENDPOINT = os.environ["AZURE_HEALTH_INSIGHTS_ENDPOINT"]

radiology_insights_client = RadiologyInsightsClient(endpoint=ENDPOINT, credential=AzureKeyCredential(KEY))

Long-Running Operations

Long-running operations are operations which consist of an initial request sent to the service to start an operation, followed by polling the service at intervals to determine whether the operation has completed or failed, and if it has succeeded, to get the result.

Methods that support healthcare analysis, custom text analysis, or multiple analyses are modeled as long-running operations. The client exposes a begin_<method-name> method that returns a poller object. Callers should wait for the operation to complete by calling result() on the poller object returned from the begin_<method-name> method. Sample code snippets are provided to illustrate using long-running operations below.

Key concepts

Radiology Insights currently supports one document from one patient. Please take a look here for more detailed information about the inferences this service produces.

Examples

Create a RadiologyInsights request and get the result using an asynchronous client

For an example how to create a client, a request and get the result see the example in the sample folder.

Get Critical Result Inference information

for patient_result in radiology_insights_result.patient_results:
    for ri_inference in patient_result.inferences:
        if (
            ri_inference.kind
            == models.RadiologyInsightsInferenceType.CRITICAL_RESULT
        ):
            critical_result = ri_inference.result
            print(
                f"Critical Result Inference found: {critical_result.description}"
            )

For detailed conceptual information of this and other inferences please read more here.

Troubleshooting

General

Health Insights Radiology Insights client library will raise exceptions defined in [Azure Core][azure_core].

Logging

This library uses the standard logging library for logging.

Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO level.

Detailed DEBUG level logging, including request/response bodies and unredacted headers, can be enabled on the client or per-operation with the logging_enable keyword argument.

See full SDK logging documentation with examples here.

Next steps

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information, see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.