There are two Tag Recommendation modes: Standard and Smart (Beta). In short, standard tag recommendations use keyword searches and is available to all users while Smart Tag Recommendations uses OpenAI for more accurate recommendations but is only available to enterprise customers.

Whether your nest is in Standard or Form-based tagging mode, Nested Knowledge automatically generates Standard Tag Recommendations for every included study. Once you have uploaded the Full Text, you can apply tags; however, you can also open the Tag Recommendations in the panel to the right of the full text.

Doing so will open a list of tag recommendations, which are the results for an automatic search by exact keyword look up across the whole full text:

To view the text that is associated with a given Tag Recommendation, click on the recommendation you're interested in. This will:

• Auto-jump the PDF to the relevant section,
• Put the tag name in the Search bar in the top left (underlined in red above), which also displays the number of occurrences of your tag,
• Highlight the text portion relevant to the recommended tag (red arrows above).

If you want to see all locations the tag occurs in the full text, you can scroll through and find relevant text contents by clicking the arrows on the Search bar.

When you are ready to apply a Tag Recommendation, all you need to do is:

• Click on it,
• Select the text content of interest (using the techniques outlined below).

Once added, the Tag Recommendation will disappear from the Tag Recommendations list and appear as an applied Tag.

Smart Tag Recommendations are an upgraded version of the above tag recommendations since it utilizes GPT 4, a language model provided by OpenAI, to search for tags and automatically highlight corresponding excerpts. Instead of searching the full text for key words alone, Smart Tag Recommendations uses artificial intelligence to search for similar or applicable terms and/or specific answers to your form-based questions. Compatible with Standard and Form-based Tagging modes, you can toggle on this feature in Admin Settings, provided you have an Enterprise subscription.

After toggling on Smart Tag Recommendations, the feature runs in the background to update all recommendations in the Tagging queue. This currently takes around 1-2 minutes per study, so it can take some time for recommendations to be generated if you have a large number of included studies. It will also automatically update when new studies are added to the queue.

Once updated, a list of tag recommendations will be shown. Similarly to Standard Tag Recommendations, clicking on any recommendation will auto-fill the excerpt box, auto-jump you to the area of the pdf based on the AI's findings and you can apply tags as normal. Below, the recommendation for the tag “Disease Symptom Scores” was selected and the associated text evidence was automatically highlighted in the full text. From here, you can apply tag or clear the selection if you do not wish to apply with this excerpt.

In Form-based Tagging mode, selecting a tag recommendation will populate the answer to the question for you as well as auto-highlighting in the full text.

In Standard mode, only the tag name is searched in the full text. In Smart mode, GPT 4 uses similarly reported terms to find the most applicable excerpt. In the below example, for the question “Where are the lesions located?”, Standard mode only searches the tag answers: “ICA terminus” “MCA” etc. Clicking on MCA, the exact name is searched in the full text only:

However, in Smart mode, a deeper dive is conducted. The AI was able to correctly identify the section where all the lesions were located, auto-selecting “Other” as the answer to encompass the multiple locations:

If you make changes to your tag hierarchy or switched tagging modes after turning on Smart Tag Recommendations, you may wish to update the recommendations accordingly. Due to expense of generating smart tag recommendations, a limited number of regenerations are allowed in a nest at this time. This limit is 1 regeneration.

Since the use of OpenAI for tag recommendations is in its beta phase, meaning it is technically feature complete but still in its early stages, there is a possibility you may run into errors. Be sure to let us know if this occurs.