Project Description: TrialLens addresses the critical problem of cancer patients missing out on clinical trial opportunities due to complex medical jargon, scattered information, and difficult navigation. Our team developed TrialLens using modern web technologies (HTML, CSS, JavaScript) with AI-powered tools to simplify trial matching and comprehension. We integrated data from public clinical trial databases to ensure medically accurate and patient-friendly content. TrialLens translates dense clinical trial data into clear, plain language, empowering users to find and understand trial options confidently.

Purpose: The motivation behind TrialLens stems from the fact that less than 5% of adult cancer patients enroll in clinical trials, often because of overwhelming or inaccessible information. We wanted to create a tool that breaks down these barriers, fostering equity and transparency in clinical trial access. By simplifying the search and understanding process, TrialLens supports patients, caregivers, and advocates in navigating the complex trial landscape. Looking forward, we envision TrialLens becoming a trusted companion throughout the cancer journey; improving trial enrollment rates, shaping better patient outcomes, and influencing healthcare policy through aggregated data insights.

How it works: Users start by inputting their personal cancer profile into TrialLens, which uses AI algorithms to match them with relevant clinical trials from up-to-date databases. The platform summarizes each trial’s goals, locations, duration, eligibility, and risks in plain, compassionate language. Users can compare trials side-by-side, understand procedures and timelines with interactive visual guides, and prepare for next steps with communication templates and 24/7 support chat. TrialLens supports multiple languages and provides daily updates on trial statuses. Our data sources include ClinicalTrials.gov and other public registries, filtered and translated to ensure meaningful, actionable insights for users.

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