1. Overview of the First Shared Task on Clinical Text Generation: RRG24 and 'Discharge Me!'
- Author
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Xu, Justin, Chen, Zhihong, Johnston, Andrew, Blankemeier, Louis, Varma, Maya, Hom, Jason, Collins, William J., Modi, Ankit, Lloyd, Robert, Hopkins, Benjamin, Langlotz, Curtis, and Delbrouck, Jean-Benoit
- Subjects
Computer Science - Computation and Language - Abstract
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!"., Comment: ACL Proceedings. BioNLP workshop
- Published
- 2024
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