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

I obtained my BSc in Human Biology from the University of Nicosia; I completed my MSc in Molecular Medicine at CING. I obtained my PhD in Neuroscience at CING. My area of expertise is neurodegenerative diseases and specifically the proteins, metabolites and lipids which are over or under abundant and the pathways affected using bioinformatics.

My areas of interest include -omics data analysis (proteomics, metabolomics and lipidomics), pathway and network analysis in neurodegenerative diseases (HD, PD, AD, ALS)

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

Data Analysis and visualisation. Being able to analysis and visualise the data assists in being able to identify correlations between variables which are important for understanding disease progression and underlying pathogenesis.

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

The use of AI can be applied to the field of neuroscience and specifically in other neurodegenerative diseases to provide insight in the genes and proteins that are dysfunctional but also influence the prognosis, diagnosis and treatment of these diseases.

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

Help create new ways to diagnose, treat, predict, prevent and cure disease. This might be achieved by: Selecting and matching patients with the most promising clinical trials. Developing and setting up remote health-monitoring devices.

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

The AI-PROGNOSIS tools will enable and assist clinicians in identifying patients at risk of PD but also be able to early diagnosis PD, so patients can receive early treatment. This can pave the way for more personalized approach to treatment based on their genetic profile.

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 can give healthcare providers an extensive overview of patient health by significantly increasing access to health-related data. They can use this information to prevent disease, lower healthcare costs and design interventions tailored for the needs of individual patients.

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

AI within the AI-PROGNOSIS project can improve healthcare and assist clinicians regarding patient prognosis, diagnosis and treatment options.

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

AI can predict the future course and outcomes of disease following diagnosis. AI can have a transformative role, by leveraging large datasets and algorithms. AI can also identify correlations which may not be immediately apparent to clinicians. Projects like AI-PROGNOSIS can indicate the importance of using AI in a clinical setting along with the clinician’s expertise.

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?

Improved prognosis and diagnosis of PD patients. This can be a stepping-stone towards personalized medicine based on the patient’s genetic profile.