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Show HN: Evidex – AI Clinical Search (RAG over PubMed/OpenAlex and SOAP Notes) (getevidex.com)

36 points by amber_raza ↗ HN
Hi HN,

I’m a solo dev building a clinical search engine to help my wife (a resident physician) and her colleagues.

The Problem: Current tools (UpToDate/OpenEvidence) are expensive, slow, or increasingly heavy with pharma ads.

The Solution: I built Evidex to be a clean, privacy-first alternative. Search Demo (GIF): https://imgur.com/a/zoUvINt

Technical Architecture (Search-Based RAG): Instead of using a traditional pre-indexed vector database (like Pinecone) which can serve stale data, I implemented a Real-time RAG pattern:

Orchestrator: A Node.js backend performs "Smart Routing" (regex/keyword analysis) on the query to decide which external APIs to hit (PubMed, Europe PMC, OpenAlex, or ClinicalTrials.gov).

Retrieval: It executes parallel fetches to these APIs at runtime to grab the top ~15 abstracts.

Local Data: Clinical guidelines are stored locally in SQLite and retrieved via full-text search (FTS) ensuring exact matches on medical terminology.

Inference: I’m using Gemini 2.5 Flash to process the concatenated abstracts. The massive context window allows me to feed it distinct search results and force strict citation mapping without latency bottlenecks.

Workflow Tools (The "Integration"): I also built a "reasoning layer" to handle complex patient histories (Case Mode) and draft documentation (SOAP Notes). Case Mode Demo (GIF): https://imgur.com/a/h01Zgkx Note Gen Demo (GIF): https://imgur.com/a/DI1S2Y0

Why no Vector DB? In medicine, "freshness" is critical. If a new trial drops today, a pre-indexed vector store might miss it. My real-time approach ensures the answer includes papers published today.

Business Model: The clinical search is free. I plan to monetize by selling billing automation tools to hospital admins later.

Feedback Request: I’d love feedback on the retrieval latency (fetching live APIs is slower than vector lookups) and the accuracy of the synthesized answers.

11 comments

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neil_naveen
FYI, You are using Clerk in development mode
bflesch
Somehow "clerk" is on my ublock origin blocklist and therefore the whole website is not loading. I didn't add "clerk" to the blocklist so it must've been added by one of the blocklists that ublock origin is subscribed to, so there must be a good reason why "clerk" is on that blocklist.

When building a product for medical audience which might care a lot about privacy maybe don't use components which are shady enough that they end up on blocklists.

Edit:

> Why no Vector DB? In medicine, "freshness" is critical. If a new trial drops today, a pre-indexed vector store might miss it. My real-time approach ensures the answer includes papers published today.

This is total rubbish - did you talk to a single medical practitioner when building this? Nobody will do new treatments on their patients if a new paper was "published" (whatever that means, just being added to some search index). These people require trusted source, experimental treatment is only done for private clients who have tried all other options.

adit_ya1
Out of curiosity, what's the prioritization of evidence (RTC Metanalysis > RTC > observational ) etc, and what's the end user benefit over a tool like OpenEvidence? You mention that other tools are expensive, slow, or increasingly heavy with pharma ads, but OpenEvidence for now seems to be pretty similiar with offerings, speed, and responses. What's your pitch as to why one should prefer this?
eoravkin
Out of curiosity, did you actually see any pharma ads on OpenEvidence?
dataviz1000
I'm working on building an AI agent that creates queries over a time-series database focused on financial data. For example, it can quantify Federal Reserve reports and generate a table showing how SPY reacted 30 minutes after, at EoD, at the next day’s open, and at the next day’s EoD. It will plan the database query and then query the data from a materialized view. It is magic!

How would biomedical researchers use tons of time-series data? A better question is: what questions are biomedical researchers asking with time-series data? I'm a lot more interested in generalized querying over time-series data than just financial data. What would be a great proof of concept?

jph
Great project. Want to contact me when you'd like to talk? I do software engineering for clinicians at a health care organization, and I'd love to have my teams try your work in their own contexts. Email joel@joelparkerhenderson.com.
OutOfHere
All such custom sites are increasingly unnecessary since modern thinking AIs like ChatGPT 5.2 Extended and Gemini 3 Pro do an incredible job surfacing good papers. In my experience, the benefit comes from using multiple AIs because they all have blind spots, and none is pareto optimal.

As a patient, sometimes I don't want the AI to have my entire medical history, as this lets me consider things from different angles. For each chat, I give it the reconstructed history that I think is sufficient. I want it to be an explorer more than a doctor.

pdyc
I like your approach of "smart routing" but using regex/keywords based approach has a problem that it does not captures semantic similarity of keywords so search with similar intents are missed, how are you handling it? or you dont need to handle it since it is for domain experts and they are likely to search based on keywords(dictionary)?
craigdalton
Excuse the blunt metaphor, but there is a risk here of turning on a fire-hose of "fresh" garbage. John Ioannidis, one of the doyens of evidence based medicine very persuasively argues - Why Most Published Research Findings Are False https://pmc.ncbi.nlm.nih.gov/articles/PMC1182327/ That is why platforms pay physicians/epidemiologists/ specialists in their field hundreds of dollars per hour to sort the good from bad papers. After my training as a doctor I did a Masters in Clinical Epidemiology and spent an afternoon each week in a tutorial that reviewed papers in the top journals - about 20-30% of them had major flaws that were either ignored or dismissed by the authors. It may be worse now. LLMs still have trouble picking up the subtleties of medical science and will miss papers with major flaws. I just did a test on a paper that is often quoted as providing evidence of excess cancer risk in communities living close to unconventional gas facilities. When I asked ChatGPT 5.2 to review the pape for evidence of increase cancer risk with a simple prompt it said the paper found such a risk. However, when I wrote a multi-discipline based prompt for 5.2 and Gemini 3 pro, it found the fatal flaw in the paper and advised it did not provide evidence. See the prompt and consider how the prompts would have to be individually developed for each paper and meta-analysis.

For review of meta-analysis you would need prompts developed by expert methodologists and discipline specialists- here is the prompt that worked: You are an environmental epidemiologist and exposure scientist, critially review this papers claim that the measured levels of unconventional gas emissions provide evidence of excess cancer risk: https://link.springer.com/article/10.1186/1476-069X-13-82