Travis Zack

Travis Zack, MD, PhD, is an Assistant Professor in the Department of Medicine at the University of California, San Francisco (UCSF), School of Medicine. His research lies at the intersection of medical informatics, artificial intelligence, and clinical data science, with a focus on leveraging large language models and natural language processing to improve healthcare delivery, oncology outcomes, and health equity.

His research interests include Medical Informatics, Artificial Intelligence in Healthcare, Natural Language Processing, Computational Oncology, Clinical Decision Support, Real-World Evidence, Health Services Research, Genomics, Public Health, and Bias in AI Systems. He applies advanced computational methods to analyze electronic health records, radiology reports, social work notes, and real-world clinical data to extract insights on patient care, treatment relevance, and social determinants of health.

The trend in his recent publications (2024–2025) highlights a strong emphasis on the application and evaluation of large language models (LLMs) in clinical settings. Topics include assessing clinical acuity in emergency departments, evaluating racial and gender biases in GPT-4, extracting contraceptive switching behaviors, and developing expert-curated datasets like CORAL to advance oncology AI. His work spans disciplines such as medical informatics, oncology, pharmacology, and public health, reflecting a multidisciplinary and translational research approach.

• No scientific awards are mentioned in the provided text.

Dr. Zack has collaborated extensively with leading researchers including Atul Butte, Julian Hong, John Gordan, and others. While no explicit mention of grants or advising is made, his leadership in multiple publications and involvement in high-impact studies suggest active research funding and mentorship roles. He is likely involved in a research lab or team focused on AI in medicine, though specific lab details are not provided.