PHAROS AI Factory Training Series – Course 5 “Trustworthy and Explainable AI in Health”, AI4Health Specialisation | on March 13th, 2026

PHAROS AI Factory announced the 5th Course of its Training Series,under the title “Trustworthy and Explainable AI in Health“, under theAI4Health Specialisation, that successfully took place on March 13th, 2026.

Presentation language: Greek

Audience: This course is suitable for AI and Machine Learning researchers and engineers, data scientists and AI Architects, healthcare professionals using AI systems, innovation managers and digital transformation leaders, policy makers, ethics officers, and compliance experts, as well as graduate and PhD students.

Location: Online via Zoom (you will get the zoom link upon registration)

Description: Artificial Intelligence is rapidly being adopted in critical sectors such as healthcare, industry and public administration, making the development of trustworthy, transparent and human-centric AI systems increasingly important. This webinar introduced the FUTURE-AI framework, presenting its six core principles — Fairness, Universality, Traceability, Usability, Robustness and Explainability — and how they apply across the entire AI lifecycle. Through expert insights and practical examples, with a particular focus on healthcare, participants explored how these principles translate into technical, organisational and governance practices aligned with the EU AI Act and international standards. The session also addressed trustworthiness by design, risk assessment and compliance strategies for high-risk AI systems.

Learning Objectives

By participating, attendees:

  • Gained a deep understanding of the FUTURE-AI principles
  • Understood the relationship between Trustworthy AI and the EU AI Act
  • Engaged directly with experts on practical implementation challenges
  • Connected theoretical principles to real-world AI use cases
  • Applied FUTURE-AI concepts in professional or project-based contexts

Learning Outcomes:

After completion, participants were able to:

  • Explain the core principles of the FUTURE-AI framework
  • Analyze risks and trust gaps in AI systems
  • Assess whether an AI system meets Trustworthy AI requirements
  • Identify compliance and governance steps for AI deployment
  • Define next steps for further learning or implementation

The course’s presentation material can be found here.

The course’s recordings can be found here.

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The project has received funding from the European High-Performance computing Joint Undertaking (JU) under grant agreement No 101234269 and the Greek Ministry of Digital Governance.

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