EDBT 2026 Demo / reviewers in the wild / expert
Jan H. Terheyden
dblp:348/5870
· DBLP profile ↗
2ranked-venue papers in the field
0as first author
2since 2021 · last 2025
—ORCID · none
Domains — venue-derived; a paper can count in several
Big Data, Cloud & Distributed Data Systems · 2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | From Retinal Pixels to Patients: Evolution of Deep Learning Research in Diabetic Retinopathy Screening
Muskaan Chopra, Lorenz Sparrenberg, Armin Berger, Sarthak Khanna, Jan H. Terheyden, Rafet Sifa |
IEEE Big Data | 5 |
| 2024 | [Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AIabstractWe 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. Maren Pielka, Jan H. Terheyden, Rafet Sifa |
IEEE Big Data | 3 |