PhD Research

Introduction

My PhD research focused on foundation models in medicine, emphasizing radiology and artificial intelligence. The cornerstone of my work was my dissertation project, which introduced the concept of comprehensive radiology AI. Additionally, I pursued projects detailed in the Other Research section.

Dissertation Research: Comprehensive Radiology AI

In my dissertation, I introduced comprehensive radiology AI as a holistic approach to AI-assisted medical image interpretation. This approach integrates diverse data types—including images, text, and structured data—to generate outputs evaluated using reproducible and automated performance metrics. My methods aim to advance radiology AI beyond bespoke solutions, enabling the development of generalist AI models for medical imaging.

Opportunistic Imaging

Central to comprehensive radiology AI is leveraging the full potential of medical imaging data beyond existing diagnostic workflows. This includes using images for tasks such as predicting future diseases or assessing biomarkers at a population scale to identify new insights. I developed AI methods to extract additional value from routine imaging data, including:

Radiology AI Evaluation

Comprehensive radiology AI requires robust evaluation methods. I introduced two key evaluation frameworks to assess AI model performance in radiology:

Other Research

In addition to my dissertation work, I collaborated on projects across various domains, expanding AI applications in medicine, including:

Acknowledgements

I was co-advised by Daniel Rubin, Akshay Chaudhari, and Curtis Langlotz. I also worked alongside other talented researchers in Stanford Radiology, including Robert Boutin and Andreas Loening, as well as external collaborators such as Leon Lenchik, Imon Banerjee and Bhavik Patel.