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

I obtained my medical degree from Ioannina University. My residency was performed Internal Medicine, Psychiatry and Neurology in Greece. I was a Neuromuscular Research Fellow at CING. I undertook a course in Neurophysiology course in hereditary polyneuropathies, a Single Fibre EMG Course, Clinical Electromyography in the United States and a Botulin Toxin Clinic course in Slovenia.

My areas of interest neurodegenerative diseases (HD, PD, AD), polyneuropathies, neuromuscular diseases and their neuroepidemiology.   

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

Clinician.

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

AI can be applied to the field of medicine and specifically in other neurodegenerative diseases to provide insight into the disease progression, disease status, prognosis, diagnosis and treatment of 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?

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 personalised medicine based on the patient’s genetic profile.