List of Ethical Requirements for the study "Co-Design of a Trustworthy AI-based Prognostic Tool for Predicting Patient Outcome in Acute Stroke"

  • Elizabeth Hofvenschioeld (Creator)
  • Adam Hilbert (Creator)
  • Cathrine K. T. Bui (Creator)
  • Susanne Bonekamp (Creator)
  • Tess Buckley (Creator)
  • Roberta Calegari (Creator)
  • Luca Alessandro Cappellini (Creator)
  • Megan Coffee (Creator)
  • Giorgio Colangelo (Creator)
  • Boris Düdder (Creator)
  • FRANCESCO DI TANO (Creator)
  • Dietmar Frey (Creator)
  • John D. Kelleher (Creator)
  • Pedro Kringen (Creator)
  • Andreane Sabourin Laflamme Laflamme (Creator)
  • Emma Moorhead (Creator)
  • Pedro Antonio Moreno Sanchez (Creator)
  • Gabriele Pluktaite (Creator)
  • Marta A Rubiera Del Fueyo (Creator)
  • Giovanni Sartor (Creator)
  • Jesmin Jahan Tithi (Creator)
  • Malte von Tottleben (Creator)
  • Ingo Werren (Creator)
  • Magnus Westerlund (Creator)
  • Renee Wurth (Creator)
  • Zicari Roberto V. (Creator)
  • Vince I. Madai (Creator)

Dataset

Description

The data was collected as part of the study “Co-Design of a Trustworthy AI-based Prognostic Tool for Predicting Patient Outcome in Acute Stroke.”  It includes ethical requirements and the associated dilemmas, issues, and risks, generated through the Z-Inspection® process. Z-Inspection® is a socio-technical co-design method for assessing the trustworthiness of artificial intelligence (AI) systems. In the study, Z-Inspection® was applied during the early design phase of an AI-based clinical decision support system (CDSS) for acute ischaemic stroke. An interdisciplinary team of Z-Inspection® experts, AI developers, and clinical stakeholders identified the ethical dilemmas, issues, and risks relevant to the planned system. Building upon this, ethical requirements were produced to address these and to provide measures for increasing the trustworthiness of the AI tool. 

This repository provides the complete dataset of the ethical requirements to support transparency and reproducibility of the Z-Inspection® method in future healthcare AI projects.

 
Date made available5 Feb 2026
PublisherZenodo

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