Anna Iurchenko
Senior UX Designer at Google Research.
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Generative AI for medical education: Insights from a case study with medical students and an AI tutor for clinical reasoning
Amy Wang
Roma Ruparel
Paul Jhun
Julie Anne Seguin
Patricia Strachan
Renee Wong
2025
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Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs), have demonstrated significant potential in clinical reasoning skills such as history-taking and differential diagnosis generation—critical aspects of medical education. This work explores how LLMs can augment medical curricula through interactive learning. We conducted a participatory design process with medical students, residents and medical education experts to co-create an AI-powered tutor prototype for clinical reasoning. As part of the co-design process, we conducted a qualitative user study, investigating learning needs and practices via interviews, and conducting concept evaluations through interactions with the prototype. Findings highlight the challenges learners face in transitioning from theoretical knowledge to practical application, and how an AI tutor can provide personalized practice and feedback. We conclude with design considerations, emphasizing the importance of context-specific knowledge and emulating positive preceptor traits, to guide the development of AI tools for medical education.
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A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy
Emma Beede
Elizabeth Baylor
Fred Hersch
Lauren Wilcox
Dr. Paisan Raumviboonsuk
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (2020)
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Deep learning algorithms promise to improve clinician workflows and patient outcomes. However, these gains have yet to be fully demonstrated in real world clinical settings. In this paper, we describe a human-centered study of a deep learning system used in clinics for the detection of diabetic eye disease. Through observation and interviews with nurses across eleven clinics across Thailand, we characterize several socio-environmental factors that impact model performance, nursing workflows, and patient experience. We find tensions between the model’s thresholds for data quality, and the quality of data that arise from an imperfect, resource-constrained environment. We discuss several advantages to conducting human-centered evaluative research alongside prospective evaluations of model accuracy, including: understanding contextual practices of clinicians and patients in order to inform system design, being able to utilize authentic clinical data in system evaluations, and understanding how the system operates within the context of clinical care prior to widespread deployment.
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