Informatics Insights with Yuan Luo
Yuan Luo, PhD, FACMI, FAIMBE, FAMIA, FIAHSI, is a professor of Preventive Medicine, Chief AI Officer at the Northwestern University Clinical and Translational Sciences Institute and Institute for AI in Medicine, and founding director of the Center for Collaborative AI in Healthcare. Globally recognized for his leadership and significant contributions to biomedical AI, Luo is visionary leader in the field and is positioned at the forefront of building next-generation biomedical informatics and collaborative AI for healthcare.
What are your research interests?
My work centers on building next-generation, collaborative AI systems for healthcare — AI that is not just powerful, but explainable, ethical and deeply integrated into real clinical and translational workflows.
The goal is to decode the complexity of human disease and enable targeted therapeutics that are both personalized and scalable. This means bridging the gap between data science and medicine — turning fragmented information into coherent insight that physicians and researchers can act upon. I’m especially passionate about proactive AI frameworks that continuously learn and improve from real-world data, transforming healthcare from reactive diagnosis to predictive, preventive, and participatory systems.
What was some of your earlier work? What are you working on now?
My earlier research focused on developing AI methods for specific data modalities — from natural language processing of clinical narratives, to machine learning on structured EHR data, to computational modeling of imaging and genomic signals. Each of these efforts advanced our understanding of how to extract reliable, interpretable knowledge from complex biomedical data streams. This body of work laid the technical and conceptual foundation for today’s multi-modal AI, where different data types are no longer analyzed in isolation but integrated into unified learning systems.
Informatics and AI in healthcare reward curiosity and collaboration more than any single technical skill. I often tell my students: Learn the language of both computers and clinicians.”
Over the years, my team and I have developed a seminal suite of AI methods, including generative AI and large language models (LLMs), that learn from and reason across multi-modal data — including multi-omics (such as single-cell and spatial transcriptomics), medical imaging, clinical data, and biomedical text — to uncover mechanisms of disease and guide targeted therapeutics.
At its core, my work is about reimagining the bench-to-bedside loop as an intelligent, continuously learning ecosystem — where AI not only assists discovery but also becomes a reliable partner in delivering better care for every patient.
Informatics and data science are very collaborative. Who do you collaborate with?
Collaboration is the lifeblood of my work. At Northwestern, I’ve fostered cross-disciplinary research with more than 40 principal investigator-level collaborators across cardiology, oncology, surgery, critical care, pediatrics, internal medicine, and transplantation — bringing together clinicians, data scientists, and engineers to tackle some of the most complex challenges in medicine.
But my role goes beyond being a collaborator — I focus on building systems that help others collaborate effectively. To that end, I launched the AI for Health (AI4H) Clinic in 2019, which guides multidisciplinary teams through every stage of AI translation, from idea inception and model development to validation, deployment and governance. The AI4H Clinic has led to real-world AI deployments in cardiology and pediatric critical care, multiple peer-reviewed publications and several NIH-funded awards across Northwestern and partner institutions.
This ecosystem-level approach to collaboration culminated in the establishment of the I.AIM Center for Collaborative AI in Health Care, which I direct. The Center serves as a connective tissue across departments and hospitals, as well as between many peer universities, empowering teams to co-create AI solutions that are not only innovative but also trustworthy and clinically grounded.
I also collaborate with national policy and methodology bodies, including the Patient-Centered Outcomes Research Institute (PCORI) and the National Quality Forum (NQF), to help shape pragmatic frameworks for the responsible and equitable adoption of AI in real-world healthcare systems.
What advice would you give to a student wanting to get into this field of study?
Informatics and AI in healthcare reward curiosity and collaboration more than any single technical skill. I often tell my students: Learn the language of both computers and clinicians. Combine mathematical rigor with empathy for how care is delivered. Seek mentors across disciplines, understand the ethics and governance of data use, and pursue opportunities to work with real-world clinical data early. The future belongs to those who can bridge silos, translating AI advances into tools clinicians trust and patients benefit from.
What are you excited about in the field of informatics right now?
I’m most excited about ushering in the era of EPIC Multi-Modal AI for Healthcare: AI that is Ethical, Proactive, Industrial, and Collaborative. Informatics is moving beyond narrow, task-specific tools toward intelligent systems that continuously learn, reason, and act across data modalities, from genomics and imaging to clinical text and real-world evidence. This transformation will enable truly proactive medicine: AI that anticipates needs, supports equitable care delivery and strengthens the feedback loop between research and clinical practice. It’s not just about prediction anymore, it’s about participation, where AI becomes a reliable partner in discovery, diagnosis and delivery of care.
My vision is to help the informatics community lead this transformation globally. We can set the standard for ethical AI governance, democratize AI literacy across disciplines, and foster industrial-scale collaboration between academia, healthcare, and technology sectors. I’ve seen how structured collaboration accelerates safe, scalable deployment. Extending that model, I envision a future where AI is as trusted and indispensable in healthcare as the stethoscope once was, powering a continuously learning, globally connected health system that serves the greater population we care for.
Join the Health Informatics Collaborative
The NUCATS Health Informatics and Data Science Collaborative fosters innovation, collaboration, and a sense of community among informaticists at Northwestern University. All are welcome to join, especially faculty, staff, students, and others on the Chicago and Evanston campuses. Regular events are convened, and opportunities are shared to spur innovation and offer support around information technology and data processes related to health. Join the Collaborative
