Summary of [vision Paper] Probot: Enhancing Patient-reported Outcome Measures For Diabetic Retinopathy Using Chatbots and Generative Ai, by Maren Pielka et al.
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI
by Maren Pielka, Tobias Schneider, Jan Terheyden, Rafet Sifa
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a large language model-based chatbot application for patient-reported outcome measures in diabetic retinopathy. By leveraging current LLM capabilities, patients can provide feedback on their quality of life and treatment progress through an interactive app. The proposed framework offers advantages over traditional methods, which rely on qualitative survey data or limited answer options. Patients will receive tailored questions about individual challenges, allowing for more detailed feedback on treatment progress. Machine learning algorithms will infer conventional PROM scores from this input, enabling clinicians to evaluate treatment status. The goal is to improve adherence and reduce subsequent vision impairment. The approach needs further validation through a survey and clinical study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special chatbot that helps patients with diabetic retinopathy share their thoughts about their quality of life and how well their treatments are working. Current methods only collect simple answers or qualitative feedback, but this new app will ask more detailed questions based on each patient’s situation. This will help doctors figure out if the treatment is helping or not. The goal is to make sure patients stick with their treatment plans better, which can prevent vision problems later on. |
Keywords
» Artificial intelligence » Large language model » Machine learning
