> For the complete documentation index, see [llms.txt](https://help.bioinsight.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.bioinsight.illumina.com/connected-insights/interpret-and-report/viewdetails-introduction/ai-literature-evidence.md). # AI Literature Evidence Connected Insights now offers a beta feature for small variants (SNV, MNV, indel) that retrieves available scientific literature supporting an oncogenic effect of the specific variant under review. It leverages a specialized text-matching model trained on biomedical literature. Each paper is presented in card format and includes the model's conclusion about what functional effect is supported by the evidence in that study: either Oncogenic or Undetermined. Papers that are judged to provide the strongest evidence of oncogenic effect are shown first (leftmost). Scroll right to view other papers, and click on a card to link out the publication in another tab. For some publications, you will require an individual or institutional subscription to access the full text.
Evidence from this feature is not used by any other algorithm in ICI. For instance, [Oncogenicity Predction](/connected-insights/interpret-and-report/viewdetails-introduction/viewdetails-oncogenicity-prediction.md) does not consider the displayed scientific literature or any Oncogenic or Undetermind label on the cards. If helpful, read the cited papers to evaluate the published evidence. --- # 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://help.bioinsight.illumina.com/connected-insights/interpret-and-report/viewdetails-introduction/ai-literature-evidence.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.