臨床文本摘要：適應大型語言模型可勝過人類專家。

摘要

在廣泛的文本數據中搜尋並總結關鍵信息，對臨床醫生如何分配時間造成了重大負擔。儘管大型語言模型（LLMs）在自然語言處理（NLP）任務中展現了巨大潛力，但它們在各種臨床摘要任務中的有效性尚未得到嚴格檢驗。在這項工作中，我們對八個LLMs應用領域適應方法，涵蓋六個數據集和四個不同的摘要任務：放射學報告、患者問題、進展記錄和醫患對話。我們的徹底定量評估揭示了模型和適應方法之間的權衡，以及LLMs最近進展可能不會帶來改進結果的情況。此外，在與六名醫生進行的臨床閱讀者研究中，我們描述了最佳適應的LLM摘要在完整性和正確性方面優於人工摘要。我們隨後的定性分析描述了LLMs和人類專家所面臨的共同挑戰。最後，我們將傳統定量NLP指標與閱讀者研究分數相關聯，以增進我們對這些指標如何與醫生偏好一致的理解。我們的研究標誌著LLMs在多個任務中優於人類專家在臨床文本摘要中的第一個證據。這意味著將LLMs整合到臨床工作流程中可以減輕文檔負擔，使臨床醫生能夠更多地專注於個性化患者護理和醫學中其他不可替代的人文方面。

English

Sifting through vast textual data and summarizing key information imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy across diverse clinical summarization tasks has not yet been rigorously examined. In this work, we employ domain adaptation methods on eight LLMs, spanning six datasets and four distinct summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not lead to improved results. Further, in a clinical reader study with six physicians, we depict that summaries from the best adapted LLM are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis delineates mutual challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and other irreplaceable human aspects of medicine.

臨床文本摘要：適應大型語言模型可勝過人類專家。

Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts

摘要

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