Integrating Machine Learning with Symbolic Reasoning to Build an Explainable AI Model for Stroke Prediction. - Nicoletta Prentzas; Andrew Nicolaides; Efthyvoulos Kyriacou; Antonis C. Kakas; Constantinos S. Pattichis (BIBE, 2019)

Extracting Explainable Assessments of Alzheimer's disease via Machine Learning on brain MRI imaging data. - Kleo G. Achilleos; Stephanos Leandrou; Nicoletta Prentzas; Panayiotis A. Kyriacou; Antonis C. Kakas; Constantinos S. Pattichis (BIBE, 2020)

Model Agnostic Explainability Techniques in Ultrasound Image Analysis. - Nicoletta Prentzas; Marios Pitsiali; Efthyvoulos Kyriacou; Andrew Nicolaides; Antonis C. Kakas; Constantinos S. Pattichis (BIBE, 2021)

Argumentation-based Explainable Machine Learning (ArgEML): a Real-life Use Case on Gynecological Cancer. - Nicoletta Prentzas; Athena Gavrielidou; Marios Neofytou; Antonis C. Kakas (ArgML@COMMA, 2022)

A Comparative Study of Explainable AI models in the Assessment of Multiple Sclerosis. - Andria Nicolaou; Nicoletta Prentzas; Christos P. Loizou; Marios Pantzaris; Antonis C. Kakas; Constantinos S. Pattichis (CAIP (2), 2023)

Argumentation-based Explainable Machine Learning ArgEML: α-Version Technical Details. - Nicoletta Prentzas (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2023)

Explainable Machine Learning via Argumentation. - Nicoletta Prentzas; Constantinos S. Pattichis; Antonis C. Kakas (xAI (3), 2023)

Explainable AI applications in the Medical Domain: a systematic review. - Nicoletta Prentzas; Antonis C. Kakas; Constantinos S. Pattichis (CoRR, 2023)

Explainable AI Modeling in the Prediction of Cardiovascular Disease Risk. - Chara Skouteli; Nicoletta Prentzas; Antonis C. Kakas; Constantinos S. Pattichis (MIE, 2024)