> For the complete documentation index, see [llms.txt](https://awsnotes.dendron.so/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://awsnotes.dendron.so/machine-learning/amazon-comprehend-medical/topics/text-analysis-apis.md). # Text analysis APIs {% hint style="info" %} This page was generated from content adapted from the [AWS Developer Guide](https://github.com/awsdocs/amazon-comprehend-medical-developer-guide.git) {% endhint %} ## Detect entities (Version 2) * **Important**\ Amazon Comprehend Medical provides confidence scores that indicate the level of confidence in the accuracy of detected entities. When you are identifying protected health information (PHI), evaluate these scores and identify the right confidence threshold for your use case. Use high-confidence thresholds in situations that require high accuracy. For certain use cases, results should be reviewed and verified by appropriately trained human reviewers. Use Amazon Comprehend Medical in patient care scenarios only after review by trained medical professionals for accuracy and exercising medical judgment. ## Detect entities * **Note**\ This version of the **DetectEntities** operation should not be used for new applications. You should use version 2 of the operation instead. All new iterations and enhancements of features will be specific to Detect Entities Version 2. For more information, see [Detect entities (Version 2)](https://github.com/kevinslin/aws-reference-notes/blob/main/services/amazon_comprehend_medical/textanalysis-entitiesv2.md). * **Important**\ Amazon Comprehend Medical provides confidence scores that indicate the level of confidence in the accuracy of detected entities. When you are identifying protected health information (PHI), evaluate these scores and identify the right confidence threshold for your use case. Use high confidence thresholds in situations that require high accuracy. For certain use cases, results should be reviewed and verified by appropriately trained human reviewers. Results from Amazon Comprehend Medical in patient care scenarios should only be used after review by trained medical professionals reviewing results for accuracy and sound medical judgment. ## Detect PHI * **Important**\ Amazon Comprehend Medical provides confidence scores that indicate the level of confidence in the accuracy of the detected entities. Evaluate these confidence scores and identify the right confidence threshold for your use case. For specific compliance use cases, we recommend that you use additional human review or other methods to confirm the accuracy of detected PHI. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://awsnotes.dendron.so/machine-learning/amazon-comprehend-medical/topics/text-analysis-apis.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.