PubMed Search Scraper

Search PubMed and export structured article metadata for literature monitoring, biomedical research, competitive intelligence, and medical AI workflows.

What does PubMed Search Scraper do?

PubMed Search Scraper turns a PubMed query into a clean dataset of public biomedical literature records.

It can collect article titles, PMIDs, abstracts, authors, journals, publication dates, DOI values, article types, MeSH terms, keywords, language, source query, and PubMed URLs.

Use it when you need repeatable literature search exports without manually copying results from PubMed.

Who is it for?

🧬 Biomedical researchers can monitor new publications for a disease, drug class, method, or intervention.

🏥 Healthcare analysts can build evidence datasets for market intelligence and clinical landscape reports.

💊 Pharma and biotech teams can track competitor research, biomarkers, indications, trial publications, and review articles.

📚 Librarians and research-support teams can automate recurring literature search exports for departments or patrons.

🤖 Medical AI teams can collect public article metadata for retrieval, evaluation, and knowledge-base workflows.

Why use this PubMed scraper?

✅ Public PubMed data, ready as JSON, CSV, Excel, or API output.

✅ Search by keyword, author, journal, date range, and article type.

✅ Optional abstract and subject-term enrichment.

✅ Stable PubMed article URLs and PMID identifiers.

✅ Built for recurring searches and monitoring jobs.

What data can you extract?

Field	Description
pmid	PubMed identifier
articleTitle	Article title
abstract	Abstract text when available
authors	Author names
journal	Journal or source name
publicationDate	Publication date from PubMed
doi	DOI when PubMed provides one
articleTypes	Publication types such as Review or Clinical Trial
meshTerms	MeSH descriptor terms
keywords	Author or index keywords
language	Article language code
url	Public PubMed article URL
sourceQuery	The query you submitted
scrapedAt	Timestamp of extraction

How much does it cost to scrape PubMed search results?

This actor uses pay-per-event pricing.

You pay a small run-start fee plus a per-article charge for each PubMed article saved to the dataset.

The default prefilled run is intentionally small so you can test output quality at low cost.

For larger recurring literature monitoring jobs, use maxItems to control volume.

How to scrape PubMed articles

1. Open the actor on Apify.
2. Enter a PubMed query such as cancer immunotherapy.
3. Choose how many articles to collect.
4. Optionally add author, journal, publication date, or article type filters.
5. Keep includeAbstracts enabled if you need abstracts, MeSH terms, and keywords.
6. Run the actor.
7. Download the dataset as JSON, CSV, Excel, XML, or HTML table.

Input options

query

Required. The PubMed search query.

Examples:

• cancer immunotherapy
• machine learning radiology
• long covid treatment
• asthma children randomized trial

maxItems

Maximum number of PubMed articles to save.

Use a small value for testing and a larger value for production monitoring.

sort

Choose relevance, publication date, most recent, first author, or journal ordering.

dateRange

Choose any time, last 1 year, last 5 years, last 10 years, or custom.

minDate and maxDate

Optional custom publication date bounds.

Accepted formats are YYYY, YYYY/MM, or YYYY/MM/DD.

journal

Optional journal filter.

Example: Nature Medicine.

author

Optional author filter.

Example: Smith J.

articleType

Optional publication type filter.

Examples: Review, Clinical Trial, Meta-Analysis, Randomized Controlled Trial, Case Reports.

includeAbstracts

When enabled, the actor includes abstracts, article types, MeSH terms, keywords, and language when available.

Turn it off for faster metadata-only exports.

Example input

{
  "query": "cancer immunotherapy",
  "maxItems": 25,
  "sort": "relevance",
  "dateRange": "5_years",
  "includeAbstracts": true
}

Example output

{
  "pmid": "42387269",
  "articleTitle": "Example PubMed article title",
  "abstract": "Abstract text when available.",
  "authors": ["Doe J", "Smith A"],
  "journal": "Journal Name",
  "publicationDate": "2026 Jul",
  "doi": "10.1000/example",
  "articleTypes": ["Journal Article"],
  "meshTerms": ["Neoplasms"],
  "keywords": ["immunotherapy"],
  "language": "eng",
  "url": "https://pubmed.ncbi.nlm.nih.gov/42387269/",
  "sourceQuery": "cancer immunotherapy",
  "scrapedAt": "2026-07-02T00:00:00.000Z"
}

Tips for better PubMed searches

🔎 Start broad, then add filters after checking result volume.

📅 Use a date range for recurring monitoring jobs.

🧑‍🔬 Use the author filter when tracking a specific researcher.

📖 Use the journal filter for targeted journal surveillance.

🏷️ Use article type filters to focus on reviews, trials, meta-analyses, or case reports.

Common use cases

• Weekly literature monitoring for disease areas.
• Competitive intelligence for pharma pipelines.
• Review article discovery for evidence summaries.
• Clinical trial publication tracking.
• Journal-specific monitoring.
• Author publication tracking.
• Dataset creation for medical NLP and retrieval workflows.
• DOI and PMID enrichment for internal bibliographies.

Integrations

Use PubMed Search Scraper with Apify datasets, webhooks, API clients, scheduled tasks, and downstream automation tools.

Typical workflows include:

• Schedule a weekly PubMed query and send new results to a database.
• Export CSV files for analysts and librarians.
• Feed article metadata into a vector database or RAG pipeline.
• Trigger a webhook when new literature-monitoring results are ready.
• Combine PubMed metadata with your internal tagging or review workflow.

API usage

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/pubmed-search-scraper').call({
  query: 'cancer immunotherapy',
  maxItems: 25,
  includeAbstracts: true,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

from apify_client import ApifyClient

client = ApifyClient('YOUR_APIFY_TOKEN')
run = client.actor('fetch_cat/pubmed-search-scraper').call(run_input={
    'query': 'cancer immunotherapy',
    'maxItems': 25,
    'includeAbstracts': True,
})

items = client.dataset(run['defaultDatasetId']).list_items().items
print(items)

cURL

curl -X POST 'https://api.apify.com/v2/acts/fetch_cat~pubmed-search-scraper/runs?token=YOUR_APIFY_TOKEN' \
  -H 'Content-Type: application/json' \
  -d '{"query":"cancer immunotherapy","maxItems":25,"includeAbstracts":true}'

MCP integration

You can use this actor from Claude and other MCP-compatible tools through the Apify MCP server.

MCP server URL format:

https://mcp.apify.com/?tools=fetch_cat/pubmed-search-scraper

Add it in Claude Code:

$claude mcp add apify-pubmed "https://mcp.apify.com/?tools=fetch_cat/pubmed-search-scraper"

Claude Desktop JSON configuration:

{
  "mcpServers": {
    "apify-pubmed": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.apify.com/?tools=fetch_cat/pubmed-search-scraper"]
    }
  }
}

Example prompts:

• "Search PubMed for the latest reviews about long COVID treatment and summarize the journals represented."
• "Collect 50 PubMed records for machine learning radiology from the last five years."
• "Find clinical trial publications about GLP-1 obesity treatment and return DOI values."

Scheduling

Create an Apify schedule to run the same PubMed query daily, weekly, or monthly.

This is useful for literature alerts, competitor monitoring, or ongoing evidence reviews.

Data freshness

PubMed is updated continuously by the source.

Each run searches the current public PubMed index and records a scrapedAt timestamp.

Limits and reliability

The actor uses public PubMed records and conservative request pacing.

Very broad queries can have hundreds of thousands of matches, so set maxItems to the volume you actually need.

If an abstract, DOI, MeSH term, or keyword is missing, it usually means PubMed does not provide that field for the article.

FAQ and troubleshooting

Why did I get fewer results than maxItems?

Your query and filters may have fewer matching PubMed records than requested.

Try removing restrictive filters or broadening the query.

Why is the abstract empty for some articles?

Not every PubMed record has an abstract available.

Some records are citations, editorials, corrections, or older articles without abstract text.

Why is DOI empty?

PubMed does not provide DOI values for every record.

Use the PMID and PubMed URL as stable identifiers when DOI is unavailable.

Legality and responsible use

This actor extracts publicly available PubMed metadata.

You are responsible for using the data in compliance with PubMed, NCBI, Apify, and applicable laws and policies.

Do not use scraped data for unlawful, misleading, or privacy-invasive purposes.

Related actors and scrapers

Explore related research and monitoring actors from the same publisher:

• ArXiv Paper Search Scraper for academic preprint monitoring.
• Google Patents Search Scraper for patent landscape research.
• GitHub Repository Search Scraper for software and open-source discovery.

Changelog

0.1

Initial version with PubMed search, metadata extraction, optional abstract enrichment, filters, and structured dataset output.

Support

If a run does not behave as expected, open an issue with your input, run ID, and a short description of what you expected to receive.

Notes

PubMed terminology can be specialized.

For best results, test your query in PubMed first, then use the same query in this actor with a suitable maxItems value.