Meet the Minds Behind the Mission
Chang Sun
Institute of Data Science, Faculty of Science and Engineering, Maastricht University
Chang Sun
Institution/Lab
Institute of Data Science, Faculty of Science and Engineering, Maastricht University
Position/Role
Assistant Professor
Major Fields of Research/Activity
Privacy-preserving health data analysis, Generative AI, Health AI
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What is your role in the REALM team at your institution?
I am the lead of WP4, managing and coordinating tasks within WP4 and making interlinks with other work packages. I actively work with WP3 and WP5 to make sure the data part is well-connected with REALM infrastructure and models parts.
What are you currently working on within the scope of REALM and what are your main goals and objectives in the project?
I am currently working on the synthetic data component which we intend to generate realistic medical data based on the real data from our demonstrators. I am also working on connecting the technical pieces (data, model, and infrastructure) and to apply to the use cases with our demonstrators. My main goals in the project are to ensure all the data including real and synthetic data are well-prepared, managed, and used for REALM infrastructure.
What are some of the most interesting findings or insights that you have gained or are expecting to gain from your research in REALM?
The most interesting insights I would like to gain are 1) if the evaluation outcome from REALM infrastructure is the same as the evaluation from the source of AI models, 2) how much synthetic medical data can help in the AI model evaluation, 3) how all the stakeholders such as health professionals, model developers, researchers, policymakers will collaborate to make such infrastructure (i.e., REALM) well-implemented and applied.
How do you see the future impact of REALM on the healthcare system?
REALM will increase the reliability and transparency of AI models in healthcare and indirectly gain more trust from patients and health professionals in AI models. Importantly, REALM starts a future where AI models can be objectively evaluated in a standardized way. REALM will also encourage AI developers, companies, and researchers to be more responsible and accountable for their AI products.
Wat makes working on the REALM project special for you personally?
I think collaborations with all partners are very valuable and special to me. All our colleagues in the project are very talented at what they are doing and open to exchanging ideas. I enjoy working with competent people and learning to be a good WP lead.
Why do you think the REALM research is important/will make a difference?
REALM is creating a new and innovative infrastructure where we can conduct transparent and accountable evaluations for AI models. Like all the other products which can be assessed and evaluated following well-recognized standards, REALM creates the same system for AI models so that our citizens and health professionals can be informed about how these models perform under standardised evaluations. REALM will build more trust from the general public in AI models.
What is one thing from your bucket list?
One thing on my bucket list is travelling from the Netherlands to China by train.
Ilias Siniosoglou
MetaMind Innovations (MINDS)
Ilias Siniosoglou
Institution/Lab
MetaMind Innovations (MINDS)
Position/Role
AI Engineer / Researcher
Major Fields of Research/Activity
Deep and Federated Learning on Next Generation IoT platforms, Explainable AI, Computer Vision and Orchestration, primarily focusing on optimization, deployment and scalability methodologies.
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What is your role in the REALM team at your institution?
My role in the REALM project is the management of the project needs, both in the administrative, organisational and implementation tasks, along with delineating the technical and research dimensions of the work, in line with the Project’s objectives. Along with my team, we strive to produce novel and quantifiable results that push the boundaries of the state-of-the-art in the scope of our work in REALM.
What are you currently working on within the scope of REALM and what are your main goals and objectives in the project?
The current work of me and my team is the creation, optimisation and curation of the Federated REALM Architecture, that defines the operational scheme and flow of the REALM platform. Along with this, we are also focusing on providing the Explainable AI (XAI) evaluations for the REALM platform that will undertake the provision of humanly interpretable explanations of the reasoning and predictions of medical AI models, in the fields of tabular, image and volumetric data. Lastly, we are involved in the synthetic data generation process for the platform and in particular with the post-market evaluation of generated data, ensuring alignment and compliance with realistic benchmarks and standards.
What are some of the most interesting findings or insights that you have gained or are expecting to gain from your research in REALM?
Through the REALM project we have found interesting and important insights about the application of AI in the different fields of medicine, while understanding better the need for a unified way to evaluate and certify them for production. This stems from the unwavering dedication of the REALM stakeholders and ethical monitoring, highlighting the requirements for robust medical AI, while creating a common glossary to bridge engineering, regulation, and medicine. Additionally, our work on XAI and Post-market evaluation continues to provide significant clues to the working and application of both AI models on medical data and the correct generation and evaluation thereof.
How do you see the future impact of REALM on healthcare system?
I feel that REALM will pave the way for an easier and more accessible assessment of medical AI and software, not only to commercial stakeholders but also to regulatory bodies and independent researchers. This will effectively enable a quality shift for these assets as they will have access to a wider range of field-specific assessment, reporting and data, that would otherwise be custom or even inaccessible to the wider public.
What makes working on the REALM project special for you personally?
Working with experts and professionals in the field is a great way to learn and deepen your connection to your work. This is especially the case for research as, for me it is not just work, it is a way to acquire a better understanding of my science. Also, it is fun. Every day I learn something new, concise in the knowledge offered by my colleagues in the project. Furthermore, the objective of REALM is on great interest to me, as it stands on the forefront of one of the most impactful sciences in the world, medicine.
Why do you think the REALM research is important/will make a difference?
It is my opinion that collaborating and developing solutions for REALM can have a great impact in both the commercial and academic worlds.
If you could have any superpower, what would it be?
If I could have any superpower, it would be the ability to fly or teleport to any place on Earth.
Bart Elen
VITO
Bart Elen
Institution/Lab
VITO
Position/Role
Principal deep learning researcher
Major Fields of Research/Activity
Medical AI
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What is your role in the REALM team at your institution?
I am the work package lead of WP5, and the Task lead of T3.4 and T5.4.
What are you currently working on within the scope of REALM and what are your main goals and objectives in the project?
My main goals and objectives are to enable a better evaluation of the performance of software as a medical device. Both, before deployment (in T3.4) and after deployment of the model in the clinical practice (in T5.4). Today, medical practitioners too often have to decide to use, or not to use, a medical device software without being accurately informed on how well this software will perform in their clinical practice on their patients. In recent years, we have seen many examples of clinicians putting trust in diagnostic or prognostic software when they should not have. Putting the health and well-being of their patients at risk.
What are some of the most interesting findings or insights that you have gained or are expecting to gain from your research in REALM?
Diagnostic and prognostic software are often presented to the world with a certain performance obtained on the test set. In practice, those models have many performances, depending on the context and patients that they get used on. I expect that REALM will make clear that only presenting an accuracy metric has limited value. REALM will teach us how to combine fitted performance metrics for different fine-grained subpopulations and usage contexts. REALM will also strengthen the inclusion of metrics such as robustness and fairness metrics to enable a better prediction of the real-world model performance.
How do you see the future impact of REALM on the healthcare system?
I have already talked with quite a few clinicians who feel disappointed. Five years ago, there was plenty of hype around many novel AI-based health applications which were going to produce a revolution in the healthcare sector. Today, many see little of this promise realized. REALM has the potential to rebuild trust in novel diagnostic and prognostic software so they can find their way to clinical practice. But this time without endangering patients' health through undeserved trust in the software.
What makes working on the REALM project special for you personally?
I used to be one of the scientists creating diagnostic and prognostic models. My team and I created deep learning models for the ophthalmology field which we brought to the market with our spin-off called MONA (https://mona.health). It is here that I got personally confronted with today’s reality that medical practitioners get very little guarantees from their software suppliers that the models will perform well in their clinical practice. Mitigating this issue became my new research interest.
Why do you think the REALM research is important/will make a difference?
Clinicians should get access to evaluations of high quality for the diagnostic and prognostic software they consider to use in their clinical practice. REALM has the mission to enable this by providing a high-quality evaluation framework using real-world and synthetic data.
What is one thing you could not live without?
High-speed Internet
Partners
- COMUNICARE - Comunicare Solutions
- ULIEGE - Université de Liège
- UANTWERPEN - University of Antwerp
- VITO - VITO
- VPHi - Virtual Physiological Human Institute for Integrative Biomedical Research
- EXUS - Exus Software Monoprosopi Etairia Periorismenis Evthinis
- MINDS - Metamind Innovations
- TRAQBEAT - Traqbeat Technologies PC