Out of curiosity, did you actually see any pharma ads on OpenEvidence?

Great question. I haven't seen banner ads on OpenEvidence yet, but the 'hidden tax' of free tools is often Publisher Bias.

Users have noted that some current tools heavily overweight citations from 'Partner Journals' (like NEJM/JAMA) because they index the full text, effectively burying better papers from non-partner journals in the vector retrieval.

My goal is strictly Neutral Retrieval. By hitting the PubMed/OpenAlex APIs live, Evidex treats a niche pediatric journal with the same relevance weight as a major publisher, ensuring the 'Long Tail' of evidence isn't drowned out by business partnerships.

this might be interesting: https://www.theinformation.com/articles/chatgpt-doctors-star...

> $150M RR on just ads, +3x from August. On <1M users.

source: https://x.com/ArfurRock/status/1999618200024076620

Whoa. $150M ARR on ads is a wild stat.

Thanks for sharing that source. It really validates the thesis that unless the user pays (SaaS), the Pharma companies are the real customers.

You built a cool product. I'm actually one of the founders of https://medisearch.io which is similar to what you are building. I think the long-tail problem that you describe can be solved in other ways than with live APIs and you may find other problems with using live APIs.

Thanks! I just took a look at MediSearch. It looks really clean.

You are definitely right that Live APIs come with their own headaches (mostly latency and rate limits).

For now, I chose this path to avoid the infrastructure overhead of maintaining a massive fresh index as a solo dev. However, I suspect that as usage grows, I will have to move toward a hybrid model where I cache or index the 'head' of the query distribution to improve performance.

Always great to meet others tackling this space. I’d love to swap notes sometime if you are open to it.

Thanks a lot! We put a lot of energy into solving hallucination. The tldr is that we have a eval set to test hallucination and tweak stuff to optimize for performance on this eval set.

Thanks! Glad that you like it:)

We haven't trained or fine-tuned on neither of USMLE or MedQA. We use several LLMs in our system, and honestly I don't think there is any single thing that I can point to which is different to how other people do it. I guess it's a combination of many small improvements.

Sorry for the vague answer. We are thinking about publishing a report at some point.

We'd be interested to hear examples or ideas of how this could be useful for you!

Thanks! Glad you liked it. Symptoms and indications --> diangosis is a bit more challenging. When we talked to some doctors, they always said that seeing the patient directly is very important. Perhaps a system like this could be a good complementary tool, or a better solution can be built on top of it.

Your point about non-urban regions is interesting. Haven't thought about that. I wonder whether it will also be much harder to show a tool like this to people there though.