ArticleClinical kidney journal2025
ECOSBot: a multicenter validation pilot study of a generative AI tool for OSCE-based nephrology training.
Article in Clinical kidney journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for affective-domain development in healthcare professions education: a systematic review.Frontiers in medicine · 2026Pooled it
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Artificial intelligence in undergraduate medical education clinical skills curricula: a scoping review of implementations since 2022.Frontiers in digital health · 2026Review
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Developing diagnostic reasoning in nephrology is particularly challenging due to its pathophysiological complexity and reliance on abstract clinical data. Objective Structured Clinical Examinations (OSCEs) are pivotal for nephrology training but remain resource-intensive and difficult to scale. Generative artificial intelligence (AI) offers a promising alternative, yet its capacity to emulate nephrology-specific OSCEs has not been formally assessed. Methods: We developed ECOSBot, a web-based tool powered by GPT-4o, to simulate both standardized patients and examiners for nephrology-focused OSCEs. In this multicenter prospective study, undergraduate medical students from five French medical schools interacted with ECOSBot across four clinical stations. All interactions were double-rated by nephrology faculty members to establish a gold standard. ECOSBot's performance was evaluated against this standard using four criteria (script coverage, authenticity, correctness and relevance) for patient simulation, and via checklists and competency-based ratings for examiner scoring. Usability was assessed using the Chatbot Usability Questionnaire (CUQ), adapted to include six items on feedback quality. Results: Ninety-one students generated 2939 prompts across 184 OSCE sessions. ECOSBot demonstrated high fidelity in patient simulation: authenticity 98.6% [95% confidence interval (CI) 98.2-99.0], correctness 98.3% (95% CI 97.9-98.7) and relevance 99.2% (95% CI 98.9-99.5), including during exchanges not explicitly covered by the pre-specified scenario. As an examiner, ECOSBot showed strong agreement with human raters on global scores [intraclass correlation coefficient (ICC) = 0.94, 95% CI 0.91-0.96], consistent across case formats, training levels and institutions. However, scoring of attitude and communication skills was less reliable (ICC = 0.44, 95% CI 0.28-0.58). Median CUQ score was 69.7/100, with 91.7% of students finding the tool highly useful for OSCE preparation in nephrology. Conclusions: ECOSBot reliably simulated both roles in nephrology OSCEs with high fidelity and strong alignment with expert rating. While challenges remain for subjective skill assessment, this tool offers a scalable and autonomous solution to enhance nephrology education.
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