The conference at Vitalis 2023 consists of several tracks with panel discussions, keynote presentations and studio talks. Most of the content will also be available online via live broadcasts and recorded lectures, available on demand.
Search the programme and customise your agenda!
You can filter by topic, seminar type, target audience or time. There are also a number of thematic tracks in the programme.
Track: MIE: Natural Language ProcessingAll sessions
Carlos Luis Parra Calderón, Denis Newman-Griffis, Riccardo Bellazzi, Stephane Meystre
Wednesday May 24, 2023 15:45 - 17:15 G1
MIE: Natural Language Processing, English, On site only, Panel, Inspiration, Advanced, Management/decision makers, Politicians, Organizational development, Technicians/IT/Developers, Researchers, Students, Actual examples (good/bad), Benefits/effects, Innovation/research, Test/validation, Information security, Ethics
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.
Anne De Hond
Thursday May 25, 2023 11:30 - 11:35 G3
MIE: Natural Language Processing, English, On site only, Presentation, Inspiration, Intermediate, Technicians/IT/Developers, Researchers, Students, Care professionals, Healthcare professionals, Innovation/research
Patients with cancer starting invasive treatment programs often develop depression that physicians struggle to recognize at an early stage. We developed a prediction model for early identification of patients at risk for depression within the first month of chemo- or radiotherapy treatment to assist physicians and healthcare workers.