AI that extracts outcome data from cancer studies, giving clinicians a fast way to search by diagnosis, drug or outcome.
For busy healthcare providers, time spent searching the literature is time taken away from patients. Literature Review uses AI to extract outcome data from cancer studies and aggregate it into one searchable resource. A clinician can select a diagnosis to see open outcome data straight away, look up a specific drug combination, or pull together the papers tied to a particular outcome. We built the platform to make a slow, manual knowledge search fast and dependable.
Cancer research is vast, fragmented and written for specialists, and the outcome data clinicians need is buried across countless studies. The task was to extract that data accurately and present it clearly, so a provider could move from a diagnosis to relevant outcomes without wading through papers by hand. Given the medical context, accuracy and clear, faithful presentation of the source data mattered above all.
We worked closely on how clinicians actually search, framing the interface around the questions they ask: a diagnosis, a drug combination, an outcome. Statistical results were extracted from multiple scientific studies and structured into a remote PostgreSQL database, with an algorithm to highlight conditions and interventions. The platform is built on Django and the Django REST Framework with a clean JavaScript and data-table front end, served through Nginx on AWS cloud infrastructure for reliable performance.
A searchable, AI-assisted resource that turns scattered cancer study outcomes into answers a clinician can find in seconds.
Outcome data pulled from multiple scientific studies and structured for search, replacing hours of manual reading.
Select a diagnosis to see aggregated open outcome data immediately, without digging through individual papers.
Search a specific drug or combination and surface the relevant evidence in one place.
Find the papers tied to a given outcome, grouped so trends are easy to read.
A remote PostgreSQL store with an algorithm that highlights conditions and interventions across the dataset.
A Django and REST platform served through Nginx on AWS, built for dependable performance as the dataset grows.
Literature Review turns a slow, manual literature search into a fast, structured one. Clinicians can move from a diagnosis to aggregated outcome data, look up drug combinations and gather supporting papers in seconds, giving them back the rarest resource in healthcare: time. The platform makes scattered research genuinely usable, on a foundation built for accuracy and scale.
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