A paper based on controllable models for simplifying medical text, co-authored by our colleague Rosni Kottekulam Vasu and external collaborators, was accepted at the 37th AAAI Conference on Artificial Intelligence! The paper is titled “Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts” and was jointly conducted with Chandrayee Basu, Michihiro Yasunaga from Stanford University, and Qian Yang from Cornell University.

The paper’s vision is an interactive automatic medical text simplification system, which can enable medical practitioners and patients to simplify the contents of a text or conversation selectively and have controllability over the type of desired textual transformations.

Rosni presented the work at AAAI, orally and on a poster and was accepted to the 2023 AAAI student scholarship and volunteer program. Congratulations!