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Updated: May 1, 2026

Artificial Intelligence Approaches to Assessing Primary Cilia
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Clinical AI is Not (Yet) Trustworthy-But It Could Be.

Ali Saad1, Sofia B Dias2, Ghada Alhussein3

  • 1AINIGMA Technologies, Leuven, Belgium.

Journal of Medical Internet Research
|April 29, 2026
PubMed
Summary

This study applied the Assessment List for Trustworthy Artificial Intelligence (ALTAI) framework in a healthcare AI project. While technical aspects like data governance and privacy were prioritized, societal impact received less attention, highlighting implementation tensions in trustworthy AI development.

Keywords:
AI-PROGNOSIS European research initiativeALTAI Frameworkassessment list for trustworthy artificial intelligenceclinical AIethical AI integrationlifecycle safeguardstrustworthy artificial intelligence (AI)

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Area of Science:

  • Health AI Governance
  • Trustworthy AI Frameworks
  • Clinical AI Adoption

Background:

  • Healthcare AI increasingly emphasizes trustworthiness beyond predictive performance.
  • Current clinical AI applications often overlook ethical and societal factors, hindering adoption.
  • Governance frameworks like ALTAI aim to operationalize trust across the AI lifecycle, but practical application data is scarce.

Purpose of the Study:

  • To describe the application of the Assessment List for Trustworthy Artificial Intelligence (ALTAI) as a governance framework within the AI-PROGNOSIS project.
  • To map ALTAI requirements to the AI lifecycle stages in a clinical AI project for Parkinson disease.
  • To examine the practical perception of ALTAI requirements by AI developers and data scientists.

Main Methods:

  • Applied the ALTAI governance framework to the Horizon Europe AI-PROGNOSIS project.
  • Mapped the seven ALTAI requirements to key stages of the AI lifecycle.
  • Conducted a structured internal survey with 10 AI developers and data scientists to assess ALTAI subdomain relevance.

Main Results:

  • Technical accuracy, data governance, and privacy were consistently rated as highly relevant by participants.
  • Societal impact was the lowest prioritized ALTAI subdomain.
  • A tension was observed where technical teams deprioritize societal concerns due to performance constraints.

Conclusions:

  • Structured governance frameworks like ALTAI can surface implementation tensions in clinical AI development.
  • Embedding multidisciplinary review across the AI lifecycle supports accountable AI development.
  • This case study provides practical guidance for integrating trust considerations into clinical AI projects, despite limitations in generalizability.