Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in clinical reports to alleviate physician workload and streamline hospital documentation. To explore these applications, we present a shared task consisting of two subtasks: (1) Radiology Report Generation (RRG24) and (2) Discharge Summary Generation ("Discharge Me!"). RRG24 involves generating the 'Findings' and 'Impression' sections of radiology reports given chest X-rays. "Discharge Me!" involves generating the 'Brief Hospital Course' and 'Discharge Instructions' sections of discharge summaries for patients admitted through the emergency department. "Discharge Me!" submissions were subsequently reviewed by a team of clinicians. Both tasks emphasize the goal of reducing clinician burnout and repetitive workloads by generating documentation. We received 201 submissions from across 8 teams for RRG24, and 211 submissions from across 16 teams for "Discharge Me!".
@article{arxiv.2409.16603,
title = {Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"},
author = {Justin Xu and Zhihong Chen and Andrew Johnston and Louis Blankemeier and Maya Varma and Jason Hom and William J. Collins and Ankit Modi and Robert Lloyd and Benjamin Hopkins and Curtis Langlotz and Jean-Benoit Delbrouck},
journal= {arXiv preprint arXiv:2409.16603},
year = {2024}
}