On Wednesday 6 November 2024, D.G.M.M. van de Sande will defend the doctoral thesis titled: Advanced-Analytics to Improve Critical Care: Moving from bytes to bedside
- Promotor
- Co-promotor
- Co-promotor
- Date
- Wednesday 6 Nov 2024, 13:00 - 14:30
- Type
- PhD defence
- Space
- Professor Andries Querido room
- Building
- Education Center
- Location
- Erasmus MC
- More information
The public defence will start exactly at 13.00pm. The doors will be closed once the public defence starts, latecomers can access the hall via the fourth floor. Given the solemn nature of the meeting, we advise not to bring children under the age of 6 to the first part of the ceremony.
A livestream link has been provided to candidate.
Below is a brief summary of the dissertation:
AI research for healthcare increased rapidly, particularly in the ICU, but still
most studies remain in development and prototyping stages and have limited
impact on patient care. To transition AI from bytes (theoretical concepts) to
bedside (practical applications) in patient treatment, it's crucial for
healthcare professionals to develop a thorough understanding of AI
technologies. Additionally, a methodical strategy is needed for the
development and implementation of these technologies, with a strong
emphasis on ethical design principles. Moreover, cross-disciplinary
collaborations are important to ensure safe and responsible use. The DESIRE
study demonstrates AI's potential in predicting safe hospital discharge. This
thesis demonstrates the consistent model performance across diverse
hospital settings and feasibility to use the system in real-world clinical
practice. Despite AI demonstrating predictive utility, testing in operational
real-world clinical settings is a crucial step to assess feasibility and safety. A
large intervention study is warranted to determine its true clinical utility.
It is evident that AI offers significant potential for enhancing healthcare
diagnostics, prognostics, and treatment methods. However, there exists a
notable gap between the development of AI models and their clinical
assessment, coupled with issues of potential biases and ethical
considerations. This gap presents substantial obstacles to the safe and
responsible deployment of AI in healthcare. Additional research is required
to establish a framework for responsible AI development and adoption in
healthcare, providing practical guidance on effectively addressing these
challenges.
