The key initial step in the research process is to analyze ongoing clinical trials to identify protein targets, associated drugs, and relevant publications. This requires research teams to meticulously read hundreds of clinical trial documents, extract key details, and verify information—a highly demanding and time-consuming task.

With Thunk.AI, this process can be automated while keeping humans in control. This sample thunk demonstrates how AI can find, extract, verify, and supplement clinical trial data, freeing up researchers to focus on higher-value analysis and decision-making.

This sample thunk has capabilities to streamline clinical trial analysis in the following ways:

In Thunk.AI, you are able to build AI tools that extend an agent's capabilities, enabling actions like accessing APIs of interest. In this thunk, you will find an AI tool to search a clinical trial database (e.g., ClinicalTrials.gov) to find and record relevant studies. Instead of manually combing through thousands of trials, AI automates the search and filtering process based on specific research criteria.

Your AI next reads (reams of) trial pages and extracts structured data using AI columns—just like spreadsheet columns, but in natural language. You can see how these instructions units in every AI column help extract protein targets, interventions & associated drugs, study details, sponsors etc. Researchers can guide the AI using simple natural language instructions—just like delegating work to a human assistant.

In some AI columns you will see that your AI performs web-based research to capture supplementary trial details, such as whether a drug is FDA-regulated, manufacturer information and supporting evidence from credible sources

For example, simply say:

"Search the web or sponsor site to check if this drug is FDA-regulated. Record URLs as evidence." and your AI follows the instructions, retrieves relevant information, and structures the output—ready for your review.

Here's a short video demonstration of this thunk:

Fred Hutch Cancer Research Center researchers used a thunk much like this one to reliably extract and synthesize information over 1,000s of trials, with AI handling data extraction, organization, and validation.

Instead of spending hours manually processing each trial, they were able to:
✅ Automate repetitive research across diverse trial formats
✅ Maintain accuracy and compliance with structured AI outputs
✅ Scale their research efforts significantly

More specifically, they achieved:

With AI handling the research-heavy work, skilled researchers could now focus on reviewing insights and applying them to decision-making. Explore the attached white paper to deep dive on this case study.

Copy & Customize this thunk: Head over to Samples and Copy the thunk and easily adapt to your specific needs. Define specific parameters for trial selection, adjust instructions, tweak AI columns, and refine data extraction rules—all without coding.

Build your own thunk: You can also design your own AI-driven research workflow tailored to your requirements and processes. Follow our step-by-step tutorial to create your own AI-powered Thunk.

Need help? Contact us at [email protected] if you need any assistance in using the platform.