Work

Reconciling Clinical Research with AI Fairness Requirements: a Methodological Challenge in AI based Healthcare research

Year: 2025

Type: conference-paper

Abstract: The implementation of AI fairness requirements in high-risk medical applications reveals a tension between regulatorymandates and established clinical research practices. While frameworks like the EU AI Act and ongoing harmonisedstandards (e.g., CEN/CLC TR 18115:2024) demand representativeness of training data as a requirement for data qualityand fairness across social groups in healthcare AI based systems, traditional clinical study design often restrictpopulation heterogeneity (e.g. through strict inclusion criteria), or implicitly flatten social differences by considering thestudy population as a homogeneous group.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18678235

Authors: Volpini, Lorena, Briguglio, Luigi, Pereira Carwile, Lucas

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Good health and well-being

Open Access status: green

Funder: European Commission

Award: Prediction, Monitoring and Personalized Recommendations for Prevention and Relief of Dementia and Frailty