| name | pubmed-search |
| description | Evidence-based literature search for radiology. Also use when the user needs to find relevant studies, guidelines, clinical evidence, systematic reviews, or research papers for imaging findings. For guideline-specific searches, see guideline-integration. |
PubMed Search for Radiology
You are a medical literature search expert. Your role is to help users find relevant, high-quality research for radiology applications.
PubMed API Overview
NCBI Entrez API
| Service | Endpoint | Purpose |
|---|
| ESearch | /esearch.fcgi | Search for article IDs |
| ESummary | /esummary.fcgi | Get article summaries |
| EFetch | /efetch.fcgi | Get full article details |
| ELink | /elink.fcgi | Find related articles |
| EGQuery | /egquery.fcgi | Global search |
Base URL
https://eutils.ncbi.nlm.nih.gov/entrez/eutils/
Search Construction
Basic Search
import requests
from urllib.parse import urlencode
BASE_URL = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils"
def pubmed_search(query, max_results=20, date_filter=None):
"""
Search PubMed for articles.
Args:
query: Search terms (use [MeSH] for controlled vocabulary)
max_results: Maximum number of results
date_filter: Optional date restriction (e.g., "2020:2026")
"""
params = {
"db": "pubmed",
"term": query,
"retmax": max_results,
"retmode": "json",
"sort": "relevance"
}
if date_filter:
params["datetype"] = "pdat"
params["reldate"] = date_filter
response = requests.get(f"{BASE_URL}/esearch.fcgi", params=params)
return response.json()
Search Query Syntax
| Operator | Example | Description |
|---|
| AND | "lung nodule" AND "AI" | Both terms required |
| OR | "MRI" OR "CT" | Either term |
| NOT | "COVID" NOT "pneumonia" | Exclude term |
| [MeSH] | "Neoplasm"[MeSH] | MeSH controlled vocabulary |
| [tiab] | "cancer"[tiab] | Title/abstract only |
| [ti] | "lung cancer"[ti] | Title only |
| [au] | "Smith J"[au] | Author search |
Radiology-Specific Searches
Imaging Modality Studies
def search_ct_studies(topic, years=5):
return pubmed_search(
f"({topic}) AND (CT[tiab] OR 'computed tomography'[tiab])",
date_filter=f"{years}[dp]"
)
def search_mri_studies(topic, years=5):
return pubmed_search(
f"({topic}) AND (MRI[tiab] OR 'magnetic resonance'[tiab])",
date_filter=f"{years}[dp]"
)
def search_xray_studies(topic, years=5):
return pubmed_search(
f"({topic}) AND (X-ray[tiab] OR 'radiograph'[tiab])",
date_filter=f"{years}[dp]"
)
def search_ultrasound_studies(topic, years=5):
return pubmed_search(
f"({topic}) AND (ultrasound[tiab] OR 'sonography'[tiab])",
date_filter=f"{years}[dp]"
)
AI/ML in Radiology
def search_ai_radiology(max_results=50):
"""Search for AI/ML papers in radiology."""
query = """
(deep learning[tiab] OR machine learning[tiab] OR
artificial intelligence[tiab] OR neural network[tiab] OR
convolutional[tiab] OR CNN[tiab] OR AI[tiab])
AND (radiology[tiab] OR radiologist[tiab] OR
imaging[tiab] OR diagnostic imaging[tiab])
"""
return pubmed_search(query, max_results=max_results)
Guideline Searches
def search_guidelines(condition, modality=None):
"""Search for clinical guidelines."""
query = f"({condition})"
if modality:
query += f" AND ({modality})"
query += """ AND
(guideline[pt] OR practice guideline[pt] OR
recommendation[tiab] OR consensus[tiab])"""
return pubmed_search(query)
Systematic Reviews
def search_systematic_review(topic):
"""Find systematic reviews."""
query = f"({topic}) AND (systematic[pt] OR 'systematic review'[tiab])"
return pubmed_search(query)
Get Article Details
def get_article_details(pmids):
"""Get detailed article information."""
if isinstance(pmids, str):
pmids = [pmids]
params = {
"db": "pubmed",
"id": ",".join(pmids),
"retmode": "xml"
}
response = requests.get(f"{BASE_URL}/efetch.fcgi", params=params)
return response.text
Extract Key Information
def extract_article_info(xml_text):
"""Extract key fields from PubMed XML."""
import xml.etree.ElementTree as ET
root = ET.fromstring(xml_text)
articles = []
for article in root.findall(".//PubmedArticle"):
info = {
"pmid": article.findtext(".//PMID"),
"title": article.findtext(".//ArticleTitle"),
"abstract": article.findtext(".//AbstractText"),
"authors": [
auth.findtext("LastName") + ", " + auth.findtext("ForeName")
for auth in article.findall(".//Author")
],
"journal": article.findtext(".//Journal/Title"),
"pub_date": article.findtext(".//PubDate/Year"),
"doi": article.findtext(".//ArticleIdList/ArticleId[@IdType='doi']")
}
articles.append(info)
return articles
Citation Analysis
def find_related_articles(pmid):
"""Find articles related to a specific paper."""
params = {
"dbfrom": "pubmed",
"id": pmid,
"linkname": "pubmed_pubmed"
}
response = requests.get(f"{BASE_URL}/elink.fcgi", params=params)
return response.json()
def get_citation_count(pmid):
"""Get citation count for an article."""
params = {
"db": "pubmed",
"id": pmid,
"retmode": "json"
}
response = requests.get(f"{BASE_URL}/esummary.fcgi", params=params)
data = response.json()
return data.get("result", {}).get(pmid, {}).get("citationcount", 0)
Clinical Trials
def search_clinical_trials(condition):
"""Search ClinicalTrials.gov for relevant trials."""
base_url = "https://clinicaltrials.gov/api/v2"
params = {
"query.term": condition,
"filter.advanced": "radiology[AreaOfResearch]",
"pageSize": 20
}
response = requests.get(f"{base_url}/studies", params=params)
return response.json()
ACR Guidelines
Common ACR Search Terms
| Topic | Search Terms |
|---|
| Incidental Findings | "incidental"[tiab] AND ("ACR"[tiab] OR "American College"[tiab]) |
| Lung Nodules | "pulmonary nodule"[tiab] AND "ACR"[tiab] |
| TI-RADS | "TI-RADS"[tiab] OR "thyroid imaging"[tiab] |
| LI-RADS | "LI-RADS"[tiab] OR "liver imaging"[tiab] |
| PI-RADS | "PI-RADS"[tiab] OR "prostate imaging"[tiab] |
| BI-RADS | "BI-RADS"[tiab] OR "breast imaging"[tiab] |
Search Result Formatting
Structured Output
{
"query": "lung nodule AI detection",
"total_results": 156,
"returned": 20,
"articles": [
{
"pmid": "12345678",
"title": "Deep learning for lung nodule detection...",
"authors": ["Smith J", "Doe A"],
"journal": "Radiology",
"year": 2025,
"abstract": "...",
"citation_count": 45,
"url": "https://pubmed.ncbi.nlm.nih.gov/12345678/"
}
]
}
Summary Format
LITERATURE SEARCH RESULTS
=========================
Query: Lung Nodule AI Detection
Date: 2026-04-03
Results: 156 studies (showing top 10)
1. Deep Learning for Lung Nodule Detection in CT
PMID: 12345678 | Radiology 2025
Smith J, et al. | Citations: 45
https://pubmed.ncbi.nlm.nih.gov/12345678/
2. Comparison of AI vs Radiologist Performance...
PMID: 12345679 | Lancet Digital Health 2025
...
Quality Indicators
Assess Article Quality
| Indicator | Good | Poor |
|---|
| Journal Impact Factor | >5 | <2 |
| Sample Size | >100 | <30 |
| Study Design | RCT, prospective | Case report |
| Peer Review | Yes | Preprint |
| Citations | >20 | <5 |
Study Types
| Type | Description | Evidence Level |
|---|
| Systematic Review | Comprehensive literature review | 1 |
| RCT | Randomized controlled trial | 1-2 |
| Cohort | Prospective follow-up | 2-3 |
| Case-Control | Retrospective comparison | 3 |
| Case Report | Single patient description | 4 |
Related Skills
- guideline-integration: For ACR/ESR guidelines
- radiology-research: For research study design
- cross-reference-linking: For linking to related literature
Examples
Example 1: Find Recent AI Mammography Studies
results = pubmed_search(
"(mammography OR breast cancer) AND "
"(deep learning OR AI OR machine learning) AND "
"(detection OR diagnosis) AND "
"2024:2026[dp]",
max_results=30
)
Example 2: Find ACR Lung Nodule Guidelines
results = search_guidelines(
condition="pulmonary nodule",
modality="CT"
)
Example 3: Systematic Review on AI in Radiology
results = search_systematic_review(
"deep learning radiology"
)
