Big Data and Explainable Artificial Intelligence for Clinical Decision Support

The rapid growth of electronic health records and other healthcare data sources presents unprecedented opportunities to use artificial intelligence (AI) to improve clinical decision-making, while also creating challenges related to interpretability, trust, bias, and implementation. This seminar will explore how big data and machine learning can be leveraged to develop predictive tools for clinical decision support, with a particular focus on explainable AI and the translation of computational predictions into actionable clinical insights. Drawing on applications in critical care and population health, the presentation will discuss approaches for developing interpretable and clinically meaningful AI decision support systems that can support earlier risk identification, improve patient safety, and ultimately strengthen healthcare delivery.