两个临床文本生成竞赛，助力医生减负。
Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"
- 设计放射科报告与出院总结生成任务，基于影像和病历自动生成文档。
- 共收到来自8队201份、16队211份提交，验证了生成效果。
- 临床团队评审结果，适合医疗AI研究者参考。
自然语言生成技术的最新进展对医疗领域具有深远影响。例如，先进系统可自动化生成临床报告中的特定章节，减轻医生工作负担，提升医院文书效率。为探索这些应用，我们组织了一项共享任务，包含两个子任务：(1) 放射科报告生成（RRG24），根据胸部X光片生成‘发现’和‘印象’部分；(2) 出院摘要生成（“Discharge Me!”），根据急诊入院患者信息生成‘简要住院经过’和‘出院指导’部分。‘Discharge Me!’的提交结果由临床团队进行评审。两项任务均以减少医生职业倦怠和重复性工作为目标。RRG24共收到8支队伍的201份提交，‘Discharge Me!’则有16支队伍提交211份成果。
原文摘要 · Abstract (English)
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!".
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