用聊天机器人让糖尿病视网膜病变患者更精准反馈治疗体验。
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI
- 基于大语言模型构建交互式聊天机器人，动态提问患者个体化问题。
- 可获取更详细的主观反馈，支持机器学习推断传统评估量表分数。
- 适合关注患者体验、提升治疗依从性的医疗AI研究者参考。
我们提出首个基于大语言模型（LLM）的聊天机器人应用，用于糖尿病视网膜病变患者报告结局（PROMs）。通过利用当前LLM的能力，患者可通过互动应用反馈生活质量与治疗进展。该框架相比现有仅收集定性数据或选项有限的静态问卷具有显著优势。PROBot LLM-PROM应用会根据患者情况提出定制化问题，获取更详尽反馈。基于这些输入，我们使用机器学习推断传统PROM评分，供临床评估治疗状态。目标是提高患者对医疗系统的依从性及治疗效果，从而减少后续视力损害。该方法需通过问卷调查和临床研究进一步验证。
原文摘要 · Abstract (English)
We present an outline of the first large language model (LLM) based chatbot application in the context of patient-reported outcome measures (PROMs) for diabetic retinopathy. By utilizing the capabilities of current LLMs, we enable patients to provide feedback about their quality of life and treatment progress via an interactive application. The proposed framework offers significant advantages over the current approach, which encompasses only qualitative collection of survey data or a static survey with limited answer options. Using the PROBot LLM-PROM application, patients will be asked tailored questions about their individual challenges, and can give more detailed feedback on the progress of their treatment. Based on this input, we will use machine learning to infer conventional PROM scores, which can be used by clinicians to evaluate the treatment status. The goal of the application is to improve adherence to the healthcare system and treatments, and thus ultimately reduce cases of subsequent vision impairment. The approach needs to be further validated using a survey and a clinical study.
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