Back
For best output, select "Paper Size" as "A4" and "Margin" as "0" or "None".
To save or print to PDF, please select Print Destination > Save as PDF, enable Background Graphics under "More Settings", then click "Save".
During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Artificial intelligence (AI) is defined as the ability of technology to mimic human intelligence processes. In nephrology, it represents a unique opportunity to enhance medical education, diagnosis, decision-making, and prognostication. However, the perception of the nephrology community in Latin America regarding its integration into academic training remains unknown.
ObjectiveTo explore the perception of the nephrology community regarding the use of AI in nephrology education through a survey conducted on the social media platform X.
A three-question cross-sectional survey was designed and disseminated through the X account of GlomConILA. The survey investigated prior exposure to AI during training, interest in incorporating AI into the nephrology curriculum, and areas with the greatest perceived potential for impact.
Question 1: During your nephrology residency, did you receive any training in AI? (n=28) → 71% responded No, 29% Yes.
Question 2: Would you like AI to be included in the nephrology curriculum? (n=25) → 80% Yes, 20% No.
Question 3: Which area of AI would have the greatest impact on nephrology training? (n=9) → 44% image analysis, 33% operational efficiency, 23% decision-making.
Most respondents reported no prior exposure to AI during their medical training, yet there was strong interest in its inclusion within the nephrology curriculum. This exploratory study highlights the enthusiasm of the Latin American renal community and underscores the need for multicenter studies to further define interests and specific educational needs for the design of AI-supported training programs in nephrology.