If you’ve ever searched for “best supplements for arthritis” or tried to decode ingredient lists on health blogs, you’ve probably landed on Examine.com or Healthline-style articles. They’re useful—but often limited by slow updates, paywalls, or one-size-fits-all summaries.
That’s exactly the problem I’m trying to solve with DigDep.com — a developer-led project to map supplement products directly to clinical research, using AI pipelines and transparent data logic.
🧪 From Ingredients to Research in One Click
Take this for example:
NOW Supplements, Glucosamine & Chondroitin with MSM – Joint Health & Comfort
On that page, you’ll find:
A list of relevant clinical trials on glucosamine, chondroitin, and MSM
Direct citations to PubMed and other research databases
A breakdown of which studies link the supplement to outcomes like reduced joint pain or improved mobility
User reviews, so you can contrast anecdotal experiences with peer-reviewed findings
It’s not just a product page — it’s a research navigator with structured science behind it.
🤖 The AI Behind It
I use a multi-model LLM pipeline to parse research papers, identify connections between ingredients and outcomes (like “arthritis relief”), and then validate those connections with human-like accuracy.
The Stack (Simplified):
Discovery: Lightweight open models scan abstracts for substance–outcome–dosage signals
Validation: GPT-4 or Claude reviews excerpts to eliminate false positives
Summary Matching: A final model cross-references the claim against the research excerpt
All this data is normalized across thousands of entries, so users can go from health goal → compound → product, or the other way around.
🧠 Why Not Just Use Examine?
Because Examine doesn’t link to actual products, and doesn’t let you filter for clinical evidence per product.
DigDep does.
Also:
Examine is paywalled; DigDep is free
Examine is slow to update; DigDep refreshes regularly via automation
Examine doesn’t map individual supplements to reviews and research; DigDep is built for it
And as developers, we can appreciate when a system is built modularly, using pipelines that evolve as the models get smarter.
🧱 It’s a Work in Progress, but Already Useful
So far, I’ve indexed:
20,000+ research papers
Hundreds of common health outcomes (e.g. arthritis, anxiety, weight loss, ADHD)
5,000+ supplements, matched by ingredients and dose
Each listing gets smarter as new research is added. The ultimate goal?
To make DigDep the most trusted and usable research-backed supplement directory out there.
💬 Try It and Tell Me What’s Missing
Here’s that example again:
NOW Glucosamine & Chondroitin – Arthritis Research & Reviews
If you're into LLM applications, health tech, or just curious about turning messy biomedical data into structured, navigable knowledge — I’d love feedback or ideas.
This is open-source in spirit (and maybe soon in code too). If you'd like to collaborate, critique, or just discuss model design — hit me up.
Thanks for reading 🙌
