Hi, my name is James Fang, and I am a graduate student in computer science at the University of Illinois Urbana-Champaign. Since 2018, when I was 14 years old, I had been interested in building applications in the biomedical and healthcare space. My goal has always been to use my technical skills to make a positive impact on humanity. I strongly believe in the potential of combining machine learning and biomedical engineering, and this is why I built this product.

While looking over the search tool at ClinicalTrials.gov I found the application quite cumbersome to use. Clinical trials, especially more recent ones, are geared towards a single demographic, with a specific condition exhibiting a certain clinical profile/phenotype. This makes keyword-based searches difficult to use, which would take up more valuable time that patients and doctors do not have.

However, with stronger, more specific search tools powered by large language models (LLMs), they enhance the search experience because semantic ideas instead of keywords are queried. Therefore, users can more quickly find relevant trials, and filter out trials that are not a good fit. In an era of (more) personalized medicine - for example, cancer drugs that target a mutation or target an expressed receptor - AI would help users to sift through the fine-grained details of the medical space.

This search tool aims to answer three questions: 1) How can doctors quickly find matching clinical trials for their patients? 2) How can researchers and regulators quickly find relevant trials to their areas of interest? And finally, 3) How can clinical trials recruit patients with matching physiological profiles?

This is my personal website here: www.jamesfang.dev. Also, if you would like to support this website, the donation button is below. Any contribution will help sustaining and expanding this project. Thank you!

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