Imperial College London, Class of 2022
Machine Learning in Focused Ultrasound: Opportunities and Challenges
During my internship, I prepared a report on the potential opportunities for machine learning in focused ultrasound. The document outlines what machine learning is, why it is exciting in healthcare, and its potential use cases and difficulties when applied to focused ultrasound. The goal of my project was to increase awareness in the focused ultrasound ecosystem about what machine learning is and why it might add value for researchers and clinicians.
In researching existing applications of machine learning in focused ultrasound, I found a variety of exciting new research that uses machine learning for problems from patient selection and outcome prediction to automated targeting and monitoring of high-intensity focused ultrasound (HIFU). I used these to inform a web resource about opportunities for machine learning in HIFU and outlined the barriers and next steps toward realizing the potential.
Why were you initially interested in working with the Foundation?
After studying biology as an undergraduate and health data science at graduate school, I am interested in finding ways to leverage emergent technology to solve problems in healthcare. I was interested in interning at the foundation to get exposure to the innovation process in a medical technology nonprofit and to understand more about the challenges and stakeholders involved.
Has your internship affected your career plans?
Yes! It has made me feel confident that there is a lot of exciting research and development going on at the intersection of biomedical technology and artificial intelligence. I am starting a job as a machine learning engineer, and in the future, I hope to apply that skillset at a biotech company or in biomedical research.
What is one tip that you would give future interns?
Ask lots of questions, and don’t be afraid to start conversations with people.