PubMed Search — Medical Papers, Abstracts & DOIs

PubMed Research Search

PubMed Search is a NexGenData tool on Apify that returns papers with abstracts, authors, citations and MeSH terms from PubMed.

Medical Papers & Data

Public biomedical literature — not medical advice.

📊 What you get

Clean JSON output (15 fields):

• pmid — PubMed ID
• title — Paper title
• authors — Author names (surname + initials)
• journal — Journal name
• pubDate — Publication date as PubMed prints it
• volume — Journal volume
• issue — Journal issue
• pages — Page range
• doi — DOI, when the paper has one
• abstract — Full abstract text
• keywords — MeSH headings and author keywords
• pubType — Publication types, e.g. Journal Article, Review
• language — Language code
• url — Link to the paper on PubMed
• searchQuery — The search that returned this paper

Pricing: $0.050 per paper (Pay-Per-Event) — about 20 papers per $1.

⚙️ Input

Field	Type	Default	What it does
query	string	"machine learning cancer diagnosis"	Medical or scientific search terms. Supports PubMed search syntax including MeSH terms, boolean operators (AND, OR, NOT), and field tags.
sortBy	string	"relevance"	'relevance' (default): PubMed's Best Match order, best for literature searches. 'newest': papers most recently added to PubMed first - use this to track new publications on a topic.
maxResults	integer	20	Maximum number of papers to return (1-100).

▶️ Example input

{
  "query": "machine learning cancer diagnosis"
}

🧾 Example output (one real row)

{
  "pmid": "36905928",
  "title": "From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.",
  "authors": [
    "Swanson K",
    "Wu E",
    "Zhang A",
    "…"
  ],
  "journal": "Cell",
  "pubDate": "2023 Apr 13",
  "volume": "186",
  "issue": "8",
  "pages": "1772-1791",
  "doi": "10.1016/j.cell.2023.01.035",
  "abstract": "Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflo…",
  "keywords": [
    "Humans",
    "Machine Learning",
    "Neoplasms",
    "…"
  ],
  "pubType": [
    "Journal Article",
    "Review",
    "Research Support, N.I.H., Extramural",
    "…"
  ],
  "language": [
    "eng"
  ],
  "url": "https://pubmed.ncbi.nlm.nih.gov/36905928/",
  "searchQuery": "machine learning cancer diagnosis"
}

Sort order: relevance or newest

sortBy controls the order of the papers returned:

• relevance (default) — PubMed's Best Match order. Best for literature reviews and finding the most relevant papers on a topic.
• newest — papers most recently added to PubMed come first. Use this to track new publications: with relevance, a scheduled run keeps returning the same well-matched older papers.

🤖 Use with AI agents

Point Claude, the OpenAI Agents SDK, an n8n flow or any MCP-aware client at it.

Sample agent prompt:

Run PubMed Research Search on my input and return the structured results.

Agentic payments (x402): Supports agentic payment via x402 — call this actor with USDC, no API key required.

🔗 Related tools — Academic research tools

Google Scholar

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A NexGenData utility actor.