ORIGINAL RESEARCH article
Front. Big Data
Sec. Machine Learning and Artificial Intelligence
This article is part of the Research TopicFrontiers in Explainable AI: Positioning XAI for Action, Human-Centered Design, Ethics, and UsabilityView all 5 articles
FunduScope: A Human-Centered, Machine Learning–Based Interactive Tool for Training Junior Ophthalmologists in Diabetic Retinopathy Detection
Provisionally accepted
- 1German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
- 2Carl von Ossietzky Universitat Oldenburg, Oldenburg, Germany
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Interpreting fundus images is an essential skill for detecting eye diseases, such as diabetic retinopathy (DR), one of the leading causes of visual impairment. However, the training of junior doctors relies on experienced ophthalmologists, who often lack the time for teaching, or on printed training materials that lack variability in examples. In this work, we present FunduScope, an interactive human-centered learning tool for training junior ophthalmologists, which is based on a pre-trained ML model for classifying DR. In a qualitative pre-study, we investigated the needs of junior doctors and identified gaps in recent learning procedures. In the main mixed-methods study, we examined the experience of 10 junior doctors with the tool and its impact on cognitive load, usability, and additional factors relevant to e-learning tools. Despite technical constraints our results confirm the potential of using an ML-based learning tool in medical education, addressing the time constraints of ophthalmologists, and providing learning independence for junior doctors. However, future work could extend the learning tool by using explainable artificial intelligence (XAI) to further support the clinical decision making of learners and exceeding the scope of this proof of concept to other ophthalmic diseases.
Keywords: Cognitive Load, Design thinking framework, e-learning, Human-centered design, learning tool, machine learning, usability
Received: 31 Jul 2025; Accepted: 02 Feb 2026.
Copyright: © 2026 Bittner, Barz and Sonntag. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Sara-Jane Bittner
Michael Barz
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