Misinterpretation of data

AI can accelerate literature screening, extraction and report drafting, but its output is only as good as the data and prompts provided. When inputs are ambiguous or incomplete, the AI may misinterpret information and return incorrect or misleading results. In a regulated environment this could lead to wrong conclusions and ultimately to inappropriate regulatory actions.

Misinterpretation of data is most likely when the AI has to guess what you want. To avoid ambiguity, be clear in your instructions and always review the AI’s suggestions before accepting them. Use the features outlined below to structure your data and validate results.

If you are using AI features in Flinn and need more detail on how they work and how to interpret their outputs, consult the following guides:

AI Extraction guide

AI Paper recommendation

AI Screening support

AI Clinical report writing

AI Detection of similar incident reports

By following the practices above and referring to the detailed guidance linked here, you can leverage Flinn’s AI features effectively while minimizing the risk of misinterpreting data.