Evidex – AI Clinical Search (RAG over PubMed/OpenAlex and SOAP Notes)
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…
In plain words
Evidex is a clinical search engine designed for physicians and medical residents that searches PubMed, OpenAlex, and clinical notes in real time using AI. Built by a solo developer, it offers a privacy-focused alternative to expensive existing tools like UpToDate, featuring a clean interface without pharmaceutical advertising. The system uses a retrieval-augmented generation approach that queries multiple medical databases simultaneously to retrieve current research abstracts and clinical guidelines.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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.
Does the same job
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Hi HN, As a researcher, I often found myself struggling with the limitations of keyword-based search when exploring PubMed papers. To address this, I created PubMed Search (https://www.pubmedisearch.com/), a tool that leverages a vector database to enable semantic search across medical research literature. Some key features: * Daily updates to ensure access to the latest articles * Semantic search using latest & greatest embedding models * Some additional useful info about the papers (tldr, journal, publication date, etc.) Hope you find it useful!
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As a grad student (and an ADHDer), I had trouble doing literature review systematically. To combat this, I made a website that finds similar papers using the meaning of the thing I am looking for. I used MixedBread's [^1] embedding model to generate vectors from the abstracts. I store and search similar vectors using Milvus [^2] and finally use Gradio [^3] to serve the frontend. I update the vector database weekly by pulling the metadata dataset from Kaggle [^4]. To speed up the search process on my free oracle instance, I binarise the embeddings and use Hamming distance as a metric. I would…
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I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
- DADocOne – A Search Engine for Healthcare2021 · docone.io · ▲49
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