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Updated: Jan 6, 2026

Artificial Intelligence Approaches to Assessing Primary Cilia
Published on: May 1, 2021
EndoCompass Project: Artificial Intelligence in Endocrinology
Guillaume Assié1,2, Gustavo Carneiro3, Eleanor Davies4
1Institut Cochin, Université Paris Cité, CNRS, INSERM, Paris, France, guillaume.assie@aphp.fr.
Background:
Endocrine science remains underrepresented in European Union research programs despite the fundamental role of hormone health in human wellbeing. Analysis of the CORDIS database reveals a persistent gap between the societal impact of endocrine disorders and their research prioritization. At national funding level, endocrine societies report limited or little attention of national research funding towards endocrinology. The EndoCompass project - a joint initiative between the European Society of Endocrinology and the European Society of Paediatric Endocrinology, aimed to identify and promote strategic research priorities in endocrine science to address critical hormone-related health challenges.
Methods:
Research priorities were established through comprehensive analysis of the EU CORDIS database covering the Horizon 2020 framework period (2014-2020). Expert analysis examined the current landscape of artificial intelligence (AI) applications in endocrinology, focussing on data-sharing frameworks, fairness considerations, and training needs.
Results:
Research priorities encompass 3 domains: establishing compliant frameworks for clinical and omic data sharing in endocrinology; developing fair and unbiased AI systems that account for demographic and clinical diversity while preventing physician de-skilling; and creating comprehensive AI training programs for endocrinologists at all career stages. Special emphasis is placed on coordinating AI initiatives across medical specialities while maintaining endocrine-specific requirements.
Conclusions:
This component of the EndoCompass project provides an evidence-based roadmap for integrating AI into endocrine practice and research. The analysis demonstrates the need for balanced approaches that leverage AI capabilities while preserving clinical expertise. The findings support strategic investment in AI infrastructure, training, and fairness-aware system development.
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