远程认知康复中自动化临床报告生成：在低资源环境下比较知识工程模板与LLM
Automated Clinical Report Generation for Remote Cognitive Remediation: Comparing Knowledge-Engineered Templates and LLMs in Low-Resource Settings
AI总结：
本文比较了在低资源环境下使用知识工程模板和LLM生成远程认知康复的自动化临床报告，探讨了临床可靠性与语言质量的权衡。
摘要：
随着认知康复治疗需求的增长，结合语音治疗师资源有限，远程康复工具的采用加速。这些系统生成大量交互数据，对临床医生来说难以高效审查。本文研究了在无参考报告的低资源环境下，针对由虚拟角色引导的居家认知康复会话的自动化临床报告生成。我们提出了并比较了两种方法：(1) 一个基于规则的模板系统，将语音治疗领域的知识编码为显式决策规则和经过验证的模板，确保临床可靠性和可追溯性；以及(2) 一个零样本LLM方法（GPT-4），旨在产生更流畅和简洁的输出。两种系统使用相同的预提取、专家验证的结构变量，使事实性比较得以控制。输出由八名语音治疗师和大四学生使用九项标准问卷评估。结果揭示了临床可靠性和语言质量之间的明显权衡。基于规则的系统在流畅性、连贯性和结果展示上得分更高，而GPT-4生成了更简洁的输出。方向性差异在评估维度上保持一致，尽管在修正后没有比较达到统计显著性，反映了专家临床评估的规模限制。基于评估员反馈，我们为远程康复环境中的临床报告系统提出了八条设计建议。更广泛地说，这项工作贡献了一种可复制的方法，结合专家提取、分类驱动生成和多维人类评估，用于低资源环境中的临床自然语言生成，并展示了如何受控比较可以指导生成AI在医疗保健中的负责任采用。
英文摘要：
The growing demand for cognitive remediation therapy, combined with limited speech therapist availability, has accelerated the adoption of remote rehabilitation tools. These systems generate large volumes of interaction data that are difficult for clinicians to review efficiently. This paper investigates automated clinical report generation for avatar-guided, home-based cognitive remediation sessions in a low-resource setting with no reference reports. We present and compare two approaches: (1) a rule-based template system encoding speech therapy domain knowledge as explicit decision rules and validated templates, ensuring clinical reliability and traceability; and (2) a zero-shot LLM-based approach (GPT-4) aimed at more fluent and concise output. Both systems use identical pre-extracted, expert-validated structured variables, enabling a controlled factual comparison. Outputs were evaluated by eight speech therapists and final-year students using a nine-criterion questionnaire. Results reveal a clear trade-off between clinical reliability and linguistic quality. The template-based system scored higher on fluidity, coherence, and results presentation, while GPT-4 produced more concise output. Directional differences are consistent across evaluation dimensions, though no comparison reached statistical significance after correction, reflecting the scale constraints of expert clinical evaluation. Based on evaluator feedback, we derive eight design recommendations for clinical reporting systems in remote rehabilitation settings. More broadly, this work contributes a replicable methodology combining expert elicitation, taxonomy-driven generation, and multi-dimensional human evaluation for clinical NLG in low-resource settings, and illustrates how controlled comparisons can inform the responsible adoption of generative AI in healthcare.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
