The Stanford Radiology AI Development and Evaluation (AIDE) Lab Summer Internship Program is designed for high school and undergraduate students passionate about artificial intelligence, machine learning, data science, and the clinical application of AI tools. This program offers hands-on experience in AI research, model development, performance evaluation, and monitoring, providing an opportunity to work alongside experienced researchers. Interns will gain practical knowledge, contribute to innovative projects, and enhance their technical and problem-solving skills.
Undergraduate Student Researcher at the AIDE Lab
“I’m excited to work at the intersection of generative AI, multimodal learning, and medical imaging, developing controllable and clinically grounded methods for image generation. I’m looking forward to contributing to responsible, translational AI research, learning from the AIDE Lab’s interdisciplinary community, and further shaping the research questions I hope to pursue in graduate school.”
High School Student Researcher at the AIDE Lab
This summer, Samhita is working with Alara on a project to release a dataset designed to evaluate the quality of radiographs, while also creating machine learning models trained on that data. She is eager to gain more hands-on experience coding and evaluating models on a real-world dataset. Beyond her own project, Samhita is looking forward to exploring the wide variety of work happening in the AIDE Lab and learning how researchers are approaching the intersections of radiology and AI from different angles.
“I’m excited about AI research because it’s a way for me to apply computer science to a wide variety of other fields, like radiology. It’s very interesting to learn about both fields at the same time.”
High School Student Researcher at the AIDE Lab
This summer, Alara is working with Samhita on a paper accompanying the release of an annotated medical dataset, while also collaborating on an AI model built to explore and evaluate the data. She is drawn to AI research for its wide range of applications and is particularly excited about its potential in medical contexts. She hopes to contribute to a real-world project with meaningful impact while deepening her understanding of AI development and application.
“I am looking forward to gaining experience with implementing and evaluating AI models, as well as learning about the many research projects that others in the lab are working on.”
Undergraduate Student Researcher at the AIDE Lab
This summer, Nathan is working on improving the performance of vendor models through test-time augmentations, a technique that enhances model accuracy without the need for retraining. He’s drawn to AI research for its potential to turn complex medical data into real solutions that can save lives, and is especially excited to learn from the wide range of interdisciplinary projects happening in the lab. Nathan hopes this experience will help him identify a specific area at the intersection of healthcare and AI to focus on as he continues into graduate studies.
“I am most excited to learn from all of the researchers in the lab. There is so much interdisciplinary work being done to improve the quality of healthcare, and I believe all of the perspectives are important.”
