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Responsible Artificial Intelligence: A Need for Healthcare Applications Passed

Wednesday May 24, 2023 15:45 - 17:15 G1

Lecturers: Carlos Luis Parra Calderón, Denis Newman-Griffis, Riccardo Bellazzi, Stephane Meystre

Track: MIE: Natural Language Processing

Remarkable progress in artificial intelligence (AI) algorithms performance, and the fast growth in “real world” data available in electronic form generate high hopes for healthcare quality, efficiency, and accessibility improvements. But this game changing progress also causes growing concerns about the effects of growing AI use and its unintended, unanticipated, or even intentionally unethical consequences. Numerous issues and limitations of the algorithms and data used in healthcare and beyond have become more visible, and several organizations and researchers have proposed advice and guidelines to help address these concerns, issues, and limitations. Principles of Responsible AI are now promoted by several important organizations and stakeholders in the AI industry, but there is a need to move these principles towards practical realization and application in real-world scenarios. This panel will address several key aspects of responsible AI in health: explainability and interpretability; bias and fairness; reliability, reusability, and efficiency; privacy and confidentiality protection.



Seminar type

On site only

Objective of lecture


Level of knowledge


Target audience

Management/decision makers
Organizational development


Actual examples (good/bad)
Information security




Stéphane Meystre, Carlos Luis Parra-Calderón, Denis Newman-Griffis, Riccardo Bellazzi


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Carlos Luis Parra Calderón Lecturer

Andalusian Health Service

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Denis Newman-Griffis Lecturer

Lecturer in Data Science
University of Sheffield

Denis Newman-Griffis (they/them) is a Lecturer in Data Science in the University of Sheffield Information School and a member of the UK Young Academy. Their work investigates the principles, processes, and practices that inform the development of data science and artificial intelligence (AI) technologies, and how transdisciplinary design thinking can help reduce and manage bias in AI systems.

They bring their work into practice at the intersection of disability and data science, and have developed AI systems for analysing information on disability experience as well as critical tools to understand how AI materialises disability. They aim to bring together diverse voices to drive disability-led design of AI and data systems and develop more inclusive, person-centred ways of working with disability data. Denis was recognized with the American Medical Informatics Association’s Doctoral Dissertation Award and is an active advocate for LGBTQIA+ support and inclusion in STEM.

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Riccardo Bellazzi Lecturer

Full Professor
University of Pavia

Full Professor of Biomedical Engineering, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
Director of Biomedical Informatics Labs, IRCCS ISC Maugeri, Pavia

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Stephane Meystre Lecturer

Scientific Director for Data Science and AI
OnePlanet & imec

Dr. Stephane Meystre is scientific director at the OnePlanet Research Center, guiding data science, AI, and data platform efforts in precision health, nutrition and behavior as well as precision agriculture, food and environment. He has a medical and biomedical informatics background, with extensive academic research and technology transfer experience in AI applications in healthcare to enable reuse of clinical data, from natural language processing applications for clinical information extraction and text de-identification, to clinical trial eligibility surveillance and predictive analytics applied to COVID-19 as examples. He has been elected fellow of the American College of Medical Informatics (FACMI), fellow of the International Academy of Health Sciences Informatics (IAHSI) and fellow of the American Medical Informatics Association (FAMIA).