Concentration in Applied AI in Systems Medicine

3 Credits | Fall Semester

This 3-credit seminar course explores how applying ethical frameworks can promote best practices and policies in systems medicine. We will analyze key components of systems medicine, including genomics, personalized/precision medicine, public health, drug development, big data, and artificial intelligence, including machine learning and natural language processing. We will also assess implementation considerations for various stakeholders, including patients, consumers, industry, research participants, underserved populations, and healthcare systems. Finally, we will consider how systems medicine approaches can be effectively leveraged to reduce health disparities and improve health equity. Students will participate in scholarly discussions, prepare brief presentations, and provide short written commentaries on several topics.

3 Credits | Fall Semester

This comprehensive 3-credit course delves into the interdisciplinary field of Systems Medicine and explores its integration with Artificial Intelligence (AI) in the context of healthcare. Students will learn how AI techniques, including machine learning, deep learning, and data analytics, can be leveraged to analyze complex biological systems, decipher disease mechanisms, and personalize medical treatments. The course covers foundational concepts in systems medicine, including omics technologies, network biology, and personalized medicine, while examining how AI methodologies can enhance our understanding of health and disease.

Throughout the course, students will explore the interdisciplinary intersection of Systems Medicine and AI within healthcare. Through engaging lectures, interactive discussions, illuminating case studies, and hands-on projects, they will delve into AI techniques such as machine learning, deep learning, and data analytics. These techniques will be applied to analyze complex biological systems and personalize medical treatments, with a particular focus on integrating real-world data extracted from Electronic Health Record (EHR) systems. By the end of the course, students will have gained practical skills in AI-enabled systems medicine and developed the ability to apply these techniques to real-world biomedical problems, leveraging EHR data to enhance their understanding and impact in healthcare.

3 Credits | Spring Semester

This course will cover key concepts in structural biology to provide students with a comprehensive understanding of the molecular basis of protein structure and function. Methodologies of structural determination such as X-ray crystallography, Nuclear Magnetic Resonance Spectroscopy, and Electron Microscopy will be covered. In addition, the course delves into the intersection of artificial intelligence (AI), drug design, and structural biology, exploring how advanced computational methods are revolutionizing the process of drug discovery. Through a blend of theoretical lectures and hands-on practical sessions, students will learn how AI techniques such as machine learning and molecular modeling are leveraged to analyze molecular structures, predict ligand-receptor interactions, and accelerate the identification of novel therapeutic compounds.