Academic and professional background, key areas of expertise, and research interests

I have obtained my PhD in Medical Genetics with expertise in high-throughput proteomics and genetics techniques. Through my PhD studies, I worked on systemic sclerosis participating in the PRECISESADs-IMI European consortium. In the first years of my post-doctoral, I worked on a Michael J. Fox project gaining hands-on experience on Next Generation Sequencing (NGS)-based methylation sequencing. In addition, in a parallel project, I analysed already available genetic data and calculated the Polygenic Risk Score (PRS) for Parkinson’s disease in the Greek-Cypriot population.  I also teach at the school of the CING through the Methodologies & Technologies Applied in Medical Genetics module.

What is your role within the AI-PROGNOSIS project, and how do your expertise and skills contribute to achieving its objectives?

Through the AI-PROGNOSIS project, I work on data analysis of large-scale datasets (e.g. AMP-PD) to calculate the PRS of PD that will be included in the AI model. My experience in high-throughput genetic techniques will help in the genetic analysis of collected samples in the project.

What future opportunities do you see for your field in similar healthcare projects?

Genetics play a key role in the improvement of personal and public health, contributing to disease prognosis, diagnosis and treatment. Similar healthcare projects are important to include genetics to assess individuals’ risk of future disease as well as prevent the disease in the next generation when possible.

What do you see as the long-term potential of the AI-PROGNOSIS project in advancing AI-driven healthcare solutions?

AI-PROGNOSIS predictive model will be a useful tool for the prediction of PD. Early prediction/prognosis refers to the early identification of the disease, even before symptoms appear. This facilitates effective management and treatment as well as may reduce the mortality from the disease.

From a clinical perspective, how do you think the AI-PROGNOSIS tools will influence the diagnosis, prognosis, and treatment of Parkinson’s disease?

Patients with PD and their families will be informed by clinicians about the AI-PROGNOSIS tools. Thus, relatives may use these tools and facilitate their early prognosis and accurate diagnosis in the future.  

How do you see digital tools, such as apps, wearables, or other technologies, transforming healthcare in projects like AI-PROGNOSIS? What specific benefits do you anticipate for patients or healthcare providers?

Digital tools may decrease the healthcare costs, detect potential health issues, enhance patient experiences, accelerate data sharing and enhance real-time health monitoring which improves decision-making accuracy.

How do you envision AI specifically supporting the AI-PROGNOSIS project, and what direct benefits do you anticipate from its integration?

AI is important for the AI-PROGNOSIS project as it enables us to collect real-time health data from the participants. It also allows the integration and analysis of data from different fields to design a multivariable predictive model for the disease risk, progression and medication. These data significantly improve care delivery and outcomes, especially when incorporated into advanced analytics tools like artificial intelligence.

What do you think is the wider impact of artificial intelligence in healthcare, and how could projects like AI-PROGNOSIS help drive this change?

Artificial intelligence could be used for patients’ monitoring. Remote monitoring of patients might be applied through intelligent telehealth such as wearables. Projects like AI-PROGNOSIS may help in the high diagnostic accuracy, treatment, prediction, prevention and cure of the disease.

How are user research and co-creation part of the AI-PROGNOSIS project, and what benefits do you expect from working together in these ways?

Encouraging different experts to work together empowers people to generate ideas and collaboratively create concepts. The model is more informative as data/factors from various fields (e.g. clinical, lifestyle, genetics) are included.

What next steps or future directions do you see for the AI-PROGNOSIS project once it's finished, and how might its outcomes shape future research or practices?

Encouraging different experts to work together empowers people to generate ideas and collaboratively create concepts. The model is more informative as data/factors from various fields (e.g. clinical, lifestyle, genetics) are included.