Rita Ribeiro is an FCT PhD Fellow at GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Instituto Superior de Engenharia do Porto (ISEP), where she is currently pursuing a PhD in Artificial Intelligence and Intelligent Systems Engineering. Her research lies at the intersection of artificial intelligence and healthcare, with interests in digital health, trustworthy and interpretable AI, evidence synthesis, and data-driven methods for clinically relevant applications. Since joining GECAD, she has been involved in funded research projects, including RM4Health and REMO, contributing to collaborative research activities in AI for health alongside academic and industrial partners. Her work has included the exploration and assessment of machine learning and deep learning approaches for health-related data, as well as participation in interdisciplinary research discussions and project-related scientific activities. Rita Ribeiro holds an MSc in Biomedical Engineering from ISEP. Her master’s dissertation, developed at Fraunhofer Portugal, was entitled "Automatic anonymization of clinical text: a study on emerging techniques and evaluation methods". Throughout her academic path, she has also developed projects in areas such as Parkinson’s disease detection using neural networks, emotion recognition in radiological examinations, and clinical decision support systems for primary care. Her scientific production includes peer-reviewed, preprint, and submitted publications in the field of AI applied to healthcare. These include a published systematic review on wearable-enabled remote health monitoring in Digital Health; a conference paper on computer vision models for pill detection to support medication adherence; an umbrella review on healthcare applications, evidence, trends, and gaps, currently available as a preprint and submitted for journal publication; and review-based work on explainable artificial intelligence in health-related contexts. Through these contributions, she has developed strong experience in scientific writing, evidence synthesis, critical appraisal of the literature, and the identification of methodological challenges and research gaps in digital health and AI for healthcare. Her current research interests include artificial intelligence in healthcare, digital health, explainability, robustness, foundation models, and the development of clinically meaningful and trustworthy decision-support approaches.