
Research the Gap
Identify gaps in medical research
What it does
A free tool that helps researchers identify gaps in medical research. Simply enter a topic (for example, Diabetes or Prostate Cancer), then choose two of six dimensions: Population, Methodology, Independent Variable, Outcome, Setting, and Study Location. The tool then generates a heatmap showing how many papers address each pair of values within the chosen dimensions. You can explore each pair and gain insight into the existing studies - if any - and start planning your next paper accordingly.
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I mapped 8.5M research papers into an interactive atlasJul 2026 · tomesphere.com · ▲85When I read papers, I have to jump between multiple tabs to find the dataset, code, videos, peer reviews, and so on. I tried to fix this with this project. It started as a project just for papers on arXiv, but after its initial success on Twitter (got like 1.9k views: the most I have gotten for a post), I have now expanded it to include other openly available papers from PubMed Central, bioRxiv, medRxiv, and eLife. These papers have been linked with their genes, proteins, diseases, drugs, clinical trials, 3D protein structures, code, and cited and similar papers. This project now has four…
- AFAI for researching personal health issues2024 · agenthost.ai · ▲38
I have some chronic medical conditions, and spend a lot of time asking about different drugs and supplements. Wanted to create a resource that others might get value from. It's trained to always provide citations, and to urge people to see their doctor before making major decisions. Open for feedback.
- IPI put PubMed in a vector DB2024 · pubmedisearch.com · ▲97
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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