{"id":124727,"date":"2025-03-13T09:45:03","date_gmt":"2025-03-13T08:45:03","guid":{"rendered":"https:\/\/ibecbarcelona.eu\/event\/net-ai-workshop-1-advances-in-biomedical-imaging-for-knowledge-extraction\/"},"modified":"2025-03-27T15:04:37","modified_gmt":"2025-03-27T14:04:37","slug":"net-ai-workshop-1-advances-in-biomedical-imaging-for-knowledge-extraction","status":"publish","type":"tribe_events","link":"https:\/\/ibecbarcelona.eu\/es\/event\/net-ai-workshop-1-advances-in-biomedical-imaging-for-knowledge-extraction\/","title":{"rendered":"Workshop 2. NET-AI: AI fundamentals and use cases in healthcare"},"content":{"rendered":"

In this second workshop of the AI for Bioengineering thematic network (NET-AI<\/a>)<\/span><\/strong>, an introduction, both theoretical and practical, to the fundamental concepts and main algorithms of artificial intelligence applied to bioengineering will be given. To this end, a first seminar will introduce the fundamental concepts of AI. This will be followed by a practical programming activity in Python, in which attendees will be able to bring their laptop to practice the implementation of simple AI models. Finally, a second seminar will offer a more specific vision of AI use cases in the healthcare field for decision-making.<\/p>\n

 <\/p>\n

Schedule programme<\/strong><\/p>\n

09:00 \u2013 Registration and Welcome<\/p>\n

09:15 \u2013 Santiago Marco, IBEC group \u201cSignal and Information Processing for Sensing Systems\u201d, \u201cAI fundamentals\u201d<\/i><\/p>\n

10: 15 \u2013 Coffee Break<\/p>\n

10:45 \u2013 Moritz Marquardt, University of Stuttgart, AI practical activity<\/u> (own computer required<\/i>)<\/p>\n

12:15 \u2013 Short break<\/p>\n

12:30 \u2013 Plenary: Alexandre Perera<\/span><\/strong><\/a>, Universitat Polit\u00e8cnica de Catalunya (UPC),\u201cFrom Ontology-Based Classifiers for Rare Conditions to Transformer-Based Models for Type 2 Diabetes \u2013 Opportunities and Risks<\/i>\u201d<\/p>\n

13:30 \u2013 Final remarks and closing<\/p>\n

To register, click here.<\/a><\/strong><\/span><\/p>\n

Health Data Science: From Ontology-Based Classifiers for Rare Conditions to Transformer-Based Models for Type 2 Diabetes \u2013 Opportunities and Risks<\/h4>\n

Alexandre Perera<\/em><\/p>\n

Health data science is transforming disease prediction and diagnosis through advanced AI models. This talk explores two key frontiers: ontology-based classifiers for rare diseases and transformer-based models for type 2 diabetes prediction. Ontology-based methods, such as those leveraging the Human Phenotype Ontology (HPO), enhance diagnostic precision in rare conditions by mapping patient-reported symptoms to expert-curated knowledge bases (Manzini et al., 2022). Initiatives like Share4Rare promote citizen science and collaborative rare disease research, fostering data sharing and patient engagement (Radu et al., 2021). Additionally, expert models aid in the early identification of inherited kidney diseases, integrating clinical expertise with machine learning (Fayos de Arizon et al., 2023) For prevalent conditions like type 2 diabetes, deep learning models excel in trajectory prediction using longitudinal health records. Attention-based encoders and deep clustering approaches have demonstrated high accuracy in forecasting disease progression and personalizing treatment strategies (Manzini et al., 2022, 2025). However, the integration of AI in clinical decision-making presents both opportunities and risks, including model bias, explainability challenges, and ethical concerns regarding data privacy. This discussion highlights the transformative potential of AI-driven disease prediction while addressing the need for robust validation, transparency, and responsible deployment\u00a0in\u00a0healthcare.<\/p>\n","protected":false},"excerpt":{"rendered":"

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