We’ve just rolled out an exciting new feature that brings the Q&A power of AskTrip directly into the Trip Database. Using AI, the system predicts questions based on a user’s search terms and the articles they view. By analysing session activity and identifying user intentions, it suggests the most relevant questions to support clinical decision-making.

If a user runs a simple search, there’s little indication of intent, so no questions are shown. Once an article is clicked, the AI gains enough context to understand the intention and generate relevant questions. In this example, a user searches for obesity children (indicating intent) and these are the suggested questions:

The user then scrolls down and clicks on the article Surgery for the treatment of obesity in children and adolescents. This indicates an interest in surgery, so the questions update (appearing above the clicked article):

As a user clicks on additional articles, the suggested questions are updated further.

In short, AskTrip transforms a user’s browsing into a dynamic, question-driven experience—helping clinicians move from search to evidence faster, with AI guiding them to the answers that matter most.