Andrew Johnston


2024

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RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports
Jean-Benoit Delbrouck | Pierre Chambon | Zhihong Chen | Maya Varma | Andrew Johnston | Louis Blankemeier | Dave Van Veen | Tan Bui | Steven Truong | Curtis Langlotz
Findings of the Association for Computational Linguistics ACL 2024

In order to enable extraction of structured clinical data from unstructured radiology reports, we introduce RadGraph-XL, a large-scale, expert-annotated dataset for clinical entity and relation extraction. RadGraph-XL consists of 2,300 radiology reports, which are annotated with over 410,000 entities and relations by board-certified radiologists. Whereas previous approaches focus solely on chest X-rays, RadGraph-XL includes data from four anatomy-modality pairs - chest CT, abdomen/pelvis CT, brain MR, and chest X-rays. Then, in order to automate structured information extraction, we use RadGraph-XL to train transformer-based models for clinical entity and relation extraction. Our evaluations include comprehensive ablation studies as well as an expert reader study that evaluates trained models on out-of-domain data. Results demonstrate that our model surpasses the performance of previous methods by up to 52% and notably outperforms GPT-4 in this domain. We release RadGraph-XL as well as our trained model to foster further innovation and research in structured clinical information extraction.

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Overview of the First Shared Task on Clinical Text Generation: RRG24 and “Discharge Me!”
Justin Xu | Zhihong Chen | Andrew Johnston | Louis Blankemeier | Maya Varma | Jason Hom | William J. Collins | Ankit Modi | Robert Lloyd | Benjamin Hopkins | Curtis Langlotz | Jean-Benoit Delbrouck
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing

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!”.