Evidence map›Paper›PMID 41133180›Full record

ArticleClinical kidney journal2025

ECOSBot: a multicenter validation pilot study of a generative AI tool for OSCE-based nephrology training.

Raphaël Bentegeac, Nans Florens, Mehdi Maanaoui, Valentin Maisons, Antoine Lanot, Mickaël Bobot, Benoît Brilland, François Glowacki, Erwin Gérard, Marc Hazzan and 3 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Raphaël BentegeacLille University, Department of Public Health - Epidemiology, Lille University Hospital Center, Lille, France.ORCID https://orcid.org/0000-0003-3608-6240
Nans FlorensNephrology Department, Hôpitaux Universitaires de Strasbourg, Strasbourg, France.ORCID https://orcid.org/0000-0002-0960-6055
Mehdi MaanaouiLille University, Department of Nephrology, Lille University Hospital Center, Lille, France.
Valentin MaisonsService de Néphrologie, CHU de Tours, Tours, France; U1246, INSERM, SPHERE, Université de Tours, Université de Nantes, Tours, Nantes, France.
Antoine LanotNormandie Université, UNICAEN, CHU de Caen Normandie, Néphrologie, CAEN, France.ORCID https://orcid.org/0000-0003-0178-7389
Mickaël BobotCentre de Néphrologie et Transplantation Rénale, Hôpital de la Conception, AP-HM, Marseille, France.ORCID https://orcid.org/0000-0002-9451-0372
Benoît BrillandService de Néphrologie-Dialyse-Transplantation, CHU Angers, Angers, France.ORCID https://orcid.org/0000-0001-9622-4014
François GlowackiLille University, Department of Nephrology, Lille University Hospital Center, Lille, France.
Erwin GérardUniversité de Lille, CHU Lille, ULR 2694-METRICS: Évaluation des Technologies de Santé et des Pratiques Médicales, Lille, France.
Marc HazzanLille University, Department of Nephrology, Lille University Hospital Center, Lille, France.
Philippe AmouyelLille University, Department of Public Health - Epidemiology, Lille University Hospital Center, Lille, France.ORCID https://orcid.org/0000-0001-9088-234X
Bastien Le GuellecUMR1167 RID-AGE, Pasteur Institute of Lille, Inserm, Lille University, Lille University Hospital Center, Lille, France.
Aghiles HamrounLille University, Department of Public Health - Epidemiology, Lille University Hospital Center, Lille, France.ORCID https://orcid.org/0000-0003-4988-206X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

generative AILLM-powered simulationmedical education technologynephrologyvirtual standardized patient

Identifiers

PMID41133180
PMCPMC12541372

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.