Free Medical Scholarly Article Schema Markup Generator — MedicalScholarlyArticle JSON-LD
Medical research shapes clinical practice, public health policy, and patient outcomes. The studies, trials, reviews, and guidelines that advance medicine are published every day in journals, repositories, and academic archives across the web — and the vast majority of that published research is invisible to the structured data systems that increasingly mediate how information is discovered, surfaced, and attributed.
That is not a minor inefficiency. It is a meaningful problem for medical authors, journals, research institutions, and the patients and clinicians who depend on accurate medical information reaching them through modern search channels.
Medical Scholarly Article Schema Markup is the structured data vocabulary designed to solve it. The MedicalScholarlyArticle type from Schema.org gives medical publishers, healthcare researchers, and academic institutions a precise, machine-readable format for declaring exactly what a piece of medical scholarship is — its subject entities, its publication type, its authors and their affiliations, its identifiers, its funding sources, its open access status, and its relationship to the journals and knowledge systems that give it scientific authority.
This free Medical Scholarly Article Schema Markup Generator by iLoveSchema creates valid, Schema.org V30.0 compliant JSON-LD for medical scholarly content in seconds. Whether you are a medical journal publisher, a university research department, a hospital academic unit, a pharmaceutical research team, a health information platform, or a clinical guideline publisher — this tool produces the complete structured data output your medical content requires.
Medical Scholarly Article Details
Generated Schema
What Is MedicalScholarlyArticle Schema Markup?
According to Schema.org, MedicalScholarlyArticle is defined as: a scholarly article in the medical domain.
Simple as that definition is, the schema type it represents is anything but simple. MedicalScholarlyArticle sits at the end of a five-level inheritance chain that gives it one of the richest property sets in the entire Schema.org vocabulary:
Thing > CreativeWork > Article > ScholarlyArticle > MedicalScholarlyArticle
At each level of this hierarchy, the type acquires new properties:
From Thing: name, description, url, identifier, image, sameAs
From CreativeWork: author, contributor, editor, publisher, datePublished, dateModified, abstract, headline, keywords, citation, license, isAccessibleForFree, funder, funding, isPartOf, about, inLanguage, audience, wordCount, sdPublisher, sdDatePublished, sdLicense
From Article: articleBody, articleSection, pageStart, pageEnd, pagination, wordCount, speakable
From ScholarlyArticle: All Article properties, now scoped to the scholarly context
From MedicalScholarlyArticle: The one unique property exclusive to this type — publicationType
That single exclusive property — publicationType — is the key that unlocks the medical scholarly context. It accepts values from the US National Library of Medicine’s MeSH (Medical Subject Headings) publication type catalogue: the controlled vocabulary that distinguishes a Randomised Controlled Trial from a Meta-Analysis, a Case Report from a Systematic Review, a Practice Guideline from a Clinical Trial Protocol. Without publicationType, structured data about a medical article is essentially just an Article. With it, the precise category of medical evidence the article represents becomes machine-readable — and that distinction matters enormously for clinical and research information systems.
Why Medical Scholarly Article Schema Matters in 2026
For most of its history, medical research publishing was an offline system: peer review, print journals, library catalogues, citation indices. The digital transition brought PDFs and DOIs but kept the same fundamental model — papers cited papers, indexes indexed papers, databases aggregated papers. Schema markup was largely irrelevant to this world.
That has changed. Three developments have made MedicalScholarlyArticle schema markup a meaningful investment for medical publishers, research institutions, and health information platforms in 2026.
AI Health Search and Answer Engines
AI-powered search platforms are handling a rapidly growing volume of health and medical queries. When someone asks Google AI Overviews, Perplexity, or ChatGPT Search “what does the current evidence say about metformin for type 2 diabetes?” or “are there randomized controlled trials supporting intermittent fasting for cardiovascular health?” — these platforms actively seek structured, machine-readable medical content to ground their answers.
Medical articles with complete schema markup — including publicationType (the type of evidence), about (the medical entities studied), author.affiliation (the institution behind the research), datePublished (recency), and citation (the evidence base) — are significantly more likely to be cited, referenced, and used as source material in AI-generated health answers than those without it.
This is not a theoretical future benefit. It is happening now, and the medical publishing institutions that are implementing structured data for their research content are building an advantage in AI-mediated health information discovery that will compound over time.
Google Health Knowledge Panels and Medical Entities
Google’s health knowledge graph — the system that generates those condition, drug, and symptom information cards in search results — draws on structured data from medical publishers. When you implement MedicalScholarlyArticle schema with properly structured about entities linking to MeSH-coded MedicalCondition, Drug, MedicalProcedure, and AnatomicalStructure objects, you are contributing structured entity data to the knowledge systems that power health information in search.
Medical publishers whose content is properly structured — with MeSH codes, author affiliations, publication types, and citation relationships declared in machine-readable format — are more credible data sources in Google’s health information ecosystem.
Academic E-E-A-T and Medical YMYL
Google’s quality evaluation systems apply their highest scrutiny to medical content under the YMYL (Your Money or Your Life) category. Medical scholarly articles are the gold standard of medical evidence — but only when Google’s systems can identify them as such. An article with publicationType: "Randomized Controlled Trial" and author.affiliation: "Department of Internal Medicine, University of Oxford" sends very different E-E-A-T signals than an anonymous blog post claiming clinical authority.
MedicalScholarlyArticle schema is the structured data mechanism that makes those authority signals — research design, institutional affiliation, peer review status, funding sources, citation relationships — machine-readable and processable by Google’s quality evaluation systems.
Medical Research Repositories and Indexing Systems
Academic repositories, institutional research archives, and medical preprint servers increasingly process structured data alongside the papers they host. Schema.org’s medical vocabulary — including MedicalScholarlyArticle, MedicalCode, and the MeSH coding system — is used by academic publishing platforms, library information systems, and research metadata aggregators as a web-accessible layer for structured bibliographic and clinical data.
Implementing MedicalScholarlyArticle schema on your published research pages makes that content machine-discoverable not just to commercial search engines but to the wider academic and biomedical information ecosystem.
The publicationType Property — The Heart of MedicalScholarlyArticle Schema
The single most important property unique to MedicalScholarlyArticle is publicationType. It accepts values from the US National Library of Medicine’s MeSH publication type catalogue — the controlled vocabulary used to classify the methodological and documentary nature of medical literature.
Understanding these publication types and choosing the correct one is the most clinically significant decision in any MedicalScholarlyArticle schema implementation. Here are the most commonly used MeSH publication types with guidance on each:
Randomized Controlled Trial — The gold standard of clinical evidence. A prospective study in which participants are randomly allocated to intervention and control groups to test a clinical hypothesis. Use when the article presents findings from a properly randomised, controlled experimental study design. This is the publication type that carries the highest evidence weight in clinical guideline development.
Meta-Analysis — A systematic synthesis of quantitative data from multiple independent studies addressing the same research question. Meta-analyses pool effect sizes across studies to produce a combined estimate with greater statistical power than any individual study. Use when the article statistically combines results from multiple prior studies.
Systematic Review — A structured literature review that uses pre-specified methods to identify, appraise, and synthesise evidence on a defined question. Unlike narrative reviews, systematic reviews follow reproducible, rigorous search and selection protocols. Often conducted alongside a Meta-Analysis; can also stand alone.
Clinical Trial — Broader than RCT — covers all prospective experimental studies in human subjects regardless of whether randomisation is used. Includes Phase I, II, III, and IV drug trials; device trials; and behavioural intervention trials. Use for prospective experimental human studies.
Review — A narrative or scoping review that summarises the existing literature on a topic. Less methodologically stringent than a Systematic Review. Covers literature reviews, narrative reviews, and scoping reviews that don’t meet the systematic review methodology threshold.
Guideline / Practice Guideline — Official recommendations for clinical practice from professional organisations, specialty societies, or government health agencies. Clinical guidelines synthesise evidence and expert consensus into actionable recommendations for clinicians.
Case Reports — Detailed documentation of individual patient cases, including presentation, diagnosis, treatment, and outcome. Case reports are the lowest on the evidence hierarchy but are valuable for documenting rare conditions, unusual presentations, or novel adverse events.
Comparative Study — A study comparing two or more interventions, conditions, or populations. May or may not be randomised.
Observational Study — A study in which researchers observe outcomes without intervening. Includes cohort studies, case-control studies, and cross-sectional surveys.
Multicenter Study — A study conducted at multiple institutions or sites. Often indicates larger sample sizes and greater generalisability than single-centre studies.
Letter — A brief communication to a journal, often responding to a previously published article or reporting a preliminary finding.
Editorial — An opinion piece from journal editors or invited experts, typically commenting on published research.
Comment — A commentary on a specific published article, often appearing in the same journal issue.
Journal Article — The general designation for published peer-reviewed research that doesn’t fit a more specific category. A safe fallback when the study design doesn’t match a more specific type.
Retracted Publication — An article that has been formally retracted by the journal following identification of errors, misconduct, or other issues. Critically important for structured data integrity — retracted articles should be clearly marked to prevent their use as valid clinical evidence.
The full MeSH publication type catalogue is maintained at the US National Library of Medicine: nlm.nih.gov/mesh/pubtypes.html.
The about Property with MeSH Medical Entities — The Clinical Context Layer
The about property in MedicalScholarlyArticle schema is where the clinical content of the article — the diseases, drugs, procedures, and anatomical structures it studies — is declared as structured entity data.
This is what separates a rich, clinically valuable schema implementation from a minimal one. A medical article marked up with only name and datePublished tells search engines and AI systems almost nothing about what it studies. A medical article with about entities linking to MeSH-coded medical entities tells these systems exactly what conditions, drugs, and procedures the research involves — in a controlled vocabulary that clinical information systems worldwide recognise.
Supported Medical Entity Types in about
Drug — For articles studying specific medications, including new drugs, drug classes, combination therapies, and pharmacological treatments. Include the MeSH code for the drug using a MedicalCode sub-object:
"about": {
"@type": "Drug",
"name": "Metformin",
"code": {
"@type": "MedicalCode",
"code": "D02.078.370.141.450",
"codingSystem": "MeSH"
}
}
MedicalCondition — For articles studying specific diseases, disorders, syndromes, or health conditions. Include the MeSH disease tree code:
"about": {
"@type": "MedicalCondition",
"name": "Diabetes Mellitus, Type 2",
"code": {
"@type": "MedicalCode",
"code": "C18.452.394.750.149",
"codingSystem": "MeSH"
}
}
MedicalProcedure — For articles about surgical procedures, diagnostic procedures, therapeutic interventions, or clinical techniques.
AnatomicalStructure — For articles in anatomy, surgery, or interventional medicine where specific anatomical locations are the primary subject.
MedicalDevice — For articles evaluating medical devices, implants, prosthetics, or diagnostic instruments.
Substance — For articles studying chemical compounds, biological agents, or other substances not classified as drugs.
Multiple about entities can be declared as an array when an article addresses several subjects simultaneously — which is common in clinical trials studying drug-disease pairs:
"about": [
{
"@type": "Drug",
"name": "Metformin",
"code": {"@type": "MedicalCode", "code": "D02.078.370.141.450", "codingSystem": "MeSH"}
},
{
"@type": "MedicalCondition",
"name": "Diabetes Mellitus, Type 2",
"code": {"@type": "MedicalCode", "code": "C18.452.394.750.149", "codingSystem": "MeSH"}
}
]
The Author Attribution System — Multiple Authors, Affiliations, and ORCID
Medical research almost always involves multiple authors, and the institutional affiliations of those authors are a critical credibility signal. MedicalScholarlyArticle schema supports both through the author property and the Person entity’s affiliation property.
Single Author:
"author": {
"@type": "Person",
"name": "Dr. Sarah Chen",
"affiliation": {
"@type": "Organization",
"name": "Department of Oncology, Johns Hopkins University School of Medicine"
}
}
Multiple Authors:
"author": [
{
"@type": "Person",
"name": "Dr. Sarah Chen",
"affiliation": {
"@type": "Organization",
"name": "Department of Oncology, Johns Hopkins University School of Medicine"
}
},
{
"@type": "Person",
"name": "Prof. James Okafor",
"affiliation": {
"@type": "Organization",
"name": "Division of Haematology, University College London"
}
}
]
With ORCID (Recommended for Academic Research): The Open Researcher and Contributor ID (ORCID) is a persistent digital identifier for researchers, resolving the author disambiguation problem that plagues academic publishing. Including an ORCID in the sameAs property of a Person entity creates an unambiguous link between the author named in the schema and their verified research identity:
"author": {
"@type": "Person",
"name": "Dr. Sarah Chen",
"sameAs": "https://orcid.org/0000-0001-2345-6789",
"affiliation": {
"@type": "Organization",
"name": "Department of Oncology, Johns Hopkins University School of Medicine",
"sameAs": "https://www.hopkinsmedicine.org/kimmel_cancer_center/"
}
}
ORCID integration in author schema is one of the highest-value additions for academic publishing platforms — it connects author entity data to the global academic identity network and is increasingly used by AI research tools to attribute findings to specific researchers.
Article Identifiers — DOI, PMID, and PMC
Medical scholarly articles are uniquely identifier-rich compared to most web content. They routinely carry DOIs (Digital Object Identifiers), PubMed IDs (PMIDs), PubMed Central IDs (PMCIDs), and sometimes additional identifiers from specific databases. Declaring these in schema creates unambiguous, globally resolvable identifiers that academic and clinical systems can process with certainty.
DOI (Digital Object Identifier) The DOI is the primary persistent identifier for scholarly articles. A properly formed DOI resolves to the canonical URL of the article on the publisher’s platform:
"identifier": {
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1056/NEJMoa1800574"
}
Or equivalently as a URL:
"sameAs": "https://doi.org/10.1056/NEJMoa1800574"
PubMed ID (PMID) For articles indexed in PubMed — the primary biomedical literature database maintained by the US National Library of Medicine:
"identifier": {
"@type": "PropertyValue",
"propertyID": "pmid",
"value": "29669239"
}
PubMed Central ID (PMCID) For open-access articles deposited in PubMed Central:
"identifier": {
"@type": "PropertyValue",
"propertyID": "pmcid",
"value": "PMC6049584"
}
Multiple identifiers can be declared as an array of PropertyValue objects:
"identifier": [
{"@type": "PropertyValue", "propertyID": "doi", "value": "10.1056/NEJMoa1800574"},
{"@type": "PropertyValue", "propertyID": "pmid", "value": "29669239"},
{"@type": "PropertyValue", "propertyID": "pmcid", "value": "PMC6049584"}
]
Journal and Publication Metadata — The isPartOf Property
A medical article does not exist in isolation — it is part of a journal, and journals are entities with their own properties: name, ISSN, publisher, volume, issue. The isPartOf property links the MedicalScholarlyArticle to its parent journal entity:
"isPartOf": {
"@type": "PublicationIssue",
"issueNumber": "4",
"isPartOf": {
"@type": "PublicationVolume",
"volumeNumber": "378",
"isPartOf": {
"@type": "Periodical",
"name": "The New England Journal of Medicine",
"issn": "0028-4793",
"publisher": {
"@type": "Organization",
"name": "Massachusetts Medical Society"
}
}
}
}
This three-level nesting — Article → Issue → Volume → Journal — creates the complete bibliographic chain that citation management systems, medical databases, and academic search engines process.
The issn property on the Periodical type is the International Standard Serial Number — the unique identifier for journals and serial publications. Use the print ISSN, eISSN, or both if available.
Open Access Status and Licensing
Open access has become a defining characteristic of modern medical publishing. Structured data for open access status helps AI systems, library discovery tools, and patient-facing health platforms identify which research is freely available without subscription barriers.
isAccessibleForFree — Boolean. Set to true for open access articles; false for subscription-only content. This single field makes open access status machine-readable across all systems that consume schema data.
license — URL of the applicable open access licence. The Creative Commons licences used by most open access medical publishing:
- CC BY 4.0:
"https://creativecommons.org/licenses/by/4.0/" - CC BY-NC 4.0:
"https://creativecommons.org/licenses/by-nc/4.0/" - CC BY-NC-ND 4.0:
"https://creativecommons.org/licenses/by-nc-nd/4.0/" - CC0 1.0 (Public Domain):
"https://creativecommons.org/publicdomain/zero/1.0/"
"isAccessibleForFree": true,
"license": "https://creativecommons.org/licenses/by/4.0/"
The audience Property — Medical Audience Types
Medical content serves different audiences, and Schema.org’s MedicalAudience type allows publishers to declare who the content is intended for. The supported enumeration values:
https://schema.org/Clinician — For content intended for practising clinical professionals: physicians, nurses, pharmacists, therapists, and other healthcare providers who deliver patient care.
https://schema.org/MedicalResearcher — For content intended for biomedical researchers, scientists, and academics engaged in health-related investigation.
https://schema.org/Patient — For content intended for patients, caregivers, and the general public seeking health information. Note: most peer-reviewed scholarly articles target Clinician or MedicalResearcher audiences rather than Patient audiences.
https://schema.org/Researcher — A broader research audience not limited to the medical domain.
"audience": {
"@type": "MedicalAudience",
"audienceType": "Clinician",
"url": "https://schema.org/Clinician"
}
Funding and Grant Declarations
Funding transparency is a cardinal principle of research ethics and a meaningful trust signal for medical publishers. Schema.org’s funder and funding properties make grant and sponsor relationships machine-readable.
funder — The organisation or individual that financially supports the research:
"funder": {
"@type": "Organization",
"name": "National Institutes of Health",
"url": "https://www.nih.gov",
"sameAs": "https://ror.org/01cwqze88"
}
funding — A specific Grant entity representing the funded award:
"funding": {
"@type": "Grant",
"name": "R01 DK123456",
"funder": {
"@type": "Organization",
"name": "National Institute of Diabetes and Digestive and Kidney Diseases",
"url": "https://www.niddk.nih.gov"
},
"identifier": {
"@type": "PropertyValue",
"propertyID": "NIH Grant Number",
"value": "R01DK123456"
}
}
Multiple funders can be declared as arrays. Research Organisations Registries (ROR) identifiers — ror.org/[identifier] — are increasingly used as authoritative persistent identifiers for research funding organisations and can be included in funder.sameAs.
How to Use the iLoveSchema Medical Scholarly Article Schema Generator
This generator is built specifically for medical publishers, healthcare research institutions, and academic health content platforms. It covers every property in the MedicalScholarlyArticle specification from Schema.org V30.0.
Step 1: Enter the Article Title (name) The full title of the article exactly as published. For articles with a subtitle, include both main title and subtitle separated by a colon.
Step 2: Select the Publication Type Choose the most accurate MeSH publication type from the dropdown. This is the most clinically significant field in the form — select Randomized Controlled Trial only for genuinely randomised experimental designs. Review for narrative literature reviews. Systematic Review for systematic literature syntheses with explicit search methodology. Meta-Analysis for quantitative pooling of effect sizes. Accuracy here directly affects how AI systems and clinical databases classify the evidence weight of the article.
Step 3: Add the Abstract Paste the full structured abstract in the description field. Medical abstracts typically follow a structured format: Background, Methods, Results, Conclusions. Include the complete text — this is the primary narrative description that AI systems use when generating research summaries.
Step 4: Add Authors and Affiliations Add each author as a separate Person entry with name, institutional affiliation, and ORCID where available. For corresponding authors, include email. List authors in the order they appear on the published article.
Step 5: Set the Medical Subjects (about) Add each medical entity the article studies — conditions, drugs, procedures, anatomical structures. For each, select the entity type and add the MeSH code if known. Find MeSH codes at: meshb.nlm.nih.gov.
Step 6: Add Article Identifiers Enter the DOI, PMID, and/or PMCID. At minimum include the DOI — it is the globally resolvable permanent identifier for the article.
Step 7: Add Journal Information Enter the journal name, ISSN, volume, issue, page range, and publisher. Use pageStart and pageEnd for simple page ranges, or pagination for complex ranges.
Step 8: Set Publication and Access Details Enter datePublished (ISO 8601 format: YYYY-MM-DD), isAccessibleForFree (true/false), and license URL for open access content.
Step 9: Add Funding Information Enter funder organisations and grant identifiers. Include ROR identifiers for funder sameAs where available.
Step 10: Add Keywords and Citations Enter MeSH subject headings or free-text keywords. Add key citations using the citation property — either as text (formatted citation string) or as DOI URLs.
Step 11: Generate, Validate, Implement Click Generate. Copy the JSON-LD. Validate at the Schema Markup Validator (validator.schema.org). Add the <script type="application/ld+json"> block to the <head> of your article page.
How to Add Medical Scholarly Article Schema to Your Website
MedicalScholarlyArticle schema is page-specific. Each article page gets its own unique schema block populated with that article’s specific data. Schema should never be added globally or applied as a template with placeholder text.
Journal Publisher Platforms (OJS, Janeway, etc.)
For platforms running on Open Journal Systems (OJS) or Janeway, schema should be added at the article template level, dynamically populated from the journal’s article metadata database. Open Journal Systems supports custom page templates and header injection — add the schema JSON-LD to the article_details template, pulling metadata from OJS’s internal data fields.
Institutional Repository Platforms (DSpace, EPrints, Figshare)
For institutional repositories, inject MedicalScholarlyArticle schema into the item display template, mapping repository metadata fields to schema properties: DC.title to name, DC.creator to author, DC.description to abstract, DC.date to datePublished, and custom fields to publicationType and about entities.
WordPress (Academic/Medical Publisher Sites)
Use WPCode to inject article-specific JSON-LD on individual post pages using conditional logic. For larger medical publishing operations on WordPress, build a custom post type for articles with custom fields for DOI, PMID, publicationType, and author affiliations — then generate the schema dynamically from those fields in the article template.
React / Next.js / Vue.js (Custom Publisher Platforms)
Generate MedicalScholarlyArticle JSON-LD server-side from your article data model. Inject it into the <head> of each article page template using your framework’s head management utility. Ensure server-side rendering (SSR) so the schema is present in the initial HTML response — critical for proper Googlebot and AI crawler processing.
Static Site Generators (Jekyll, Hugo, Eleventy)
For academic sites built with static site generators, generate schema as part of the build process from your article data files (YAML frontmatter, JSON data files). Include the generated schema in your article layout template.
Complete Medical Scholarly Article Schema (JSON-LD) Example
A production-ready MedicalScholarlyArticle schema block for a randomized controlled trial on cardiovascular outcomes.
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "MedicalScholarlyArticle",
"name": "Empagliflozin and Cardiovascular Outcomes in Patients with Type 2 Diabetes and Established Cardiovascular Disease: A Randomised Controlled Trial",
"headline": "Empagliflozin and Cardiovascular Outcomes in Type 2 Diabetes",
"alternativeHeadline": "EMPA-CV: Empagliflozin Cardiovascular Outcomes Trial",
"description": "Background: Cardiovascular disease is the leading cause of mortality in patients with type 2 diabetes mellitus. Empagliflozin, an SGLT2 inhibitor, has demonstrated promising cardiovascular effects in preliminary studies. Methods: We conducted a multicentre, double-blind, randomised controlled trial enrolling 8,512 patients with type 2 diabetes and established atherosclerotic cardiovascular disease. Participants were randomised 1:1 to receive empagliflozin 10mg or placebo once daily. The primary composite endpoint was major adverse cardiovascular events (MACE). Results: Over a median follow-up of 3.2 years, empagliflozin significantly reduced the rate of the primary MACE composite (11.4% vs 14.2%; hazard ratio 0.79, 95% CI 0.71–0.89; p<0.001). Hospitalisation for heart failure was also significantly reduced (2.4% vs 4.1%; HR 0.60). Conclusions: Empagliflozin significantly reduced the risk of major adverse cardiovascular events and hospitalisation for heart failure in patients with type 2 diabetes and established cardiovascular disease.",
"abstract": "Background: Cardiovascular disease is the leading cause of mortality in patients with type 2 diabetes. We evaluated the cardiovascular safety and efficacy of empagliflozin, an SGLT2 inhibitor. Methods: Double-blind, randomised, placebo-controlled trial in 8,512 patients. Primary endpoint: composite of cardiovascular death, non-fatal myocardial infarction, or non-fatal stroke. Results: Empagliflozin significantly reduced MACE vs placebo (HR 0.79, p<0.001) and hospitalisation for heart failure (HR 0.60, p<0.001). Conclusions: Empagliflozin reduces cardiovascular events in high-risk patients with type 2 diabetes.",
"publicationType": "Randomized Controlled Trial",
"datePublished": "2024-11-14",
"dateModified": "2024-11-20",
"url": "https://example-cardiojournal.com/articles/empagliflozin-cv-outcomes-2024",
"inLanguage": "en",
"wordCount": 4218,
"pageStart": "1841",
"pageEnd": "1853",
"author": [
{
"@type": "Person",
"name": "Professor David Kellerman",
"sameAs": "https://orcid.org/0000-0002-1234-5678",
"affiliation": {
"@type": "Organization",
"name": "Department of Cardiology, University Medical Centre Hamburg",
"sameAs": "https://ror.org/04b31q352"
},
"email": "d.kellerman@umc-hamburg.de"
},
{
"@type": "Person",
"name": "Dr. Amira Hassan",
"sameAs": "https://orcid.org/0000-0003-9876-5432",
"affiliation": {
"@type": "Organization",
"name": "Division of Endocrinology and Metabolism, Cairo University School of Medicine"
}
},
{
"@type": "Person",
"name": "Dr. Raj Patel",
"sameAs": "https://orcid.org/0000-0001-5678-9012",
"affiliation": {
"@type": "Organization",
"name": "Cardiology Research Unit, King's College Hospital, London",
"sameAs": "https://ror.org/05k31qx86"
}
}
],
"editor": {
"@type": "Person",
"name": "Professor Marcus Almeida",
"affiliation": {
"@type": "Organization",
"name": "International Journal of Cardiology"
}
},
"publisher": {
"@type": "Organization",
"name": "International Cardiology Publishing Group",
"url": "https://example-cardiojournal.com"
},
"isPartOf": {
"@type": "PublicationIssue",
"issueNumber": "22",
"isPartOf": {
"@type": "PublicationVolume",
"volumeNumber": "47",
"isPartOf": {
"@type": "Periodical",
"name": "International Journal of Cardiology",
"issn": "0167-5273",
"publisher": {
"@type": "Organization",
"name": "International Cardiology Publishing Group"
}
}
}
},
"about": [
{
"@type": "Drug",
"name": "Empagliflozin",
"code": {
"@type": "MedicalCode",
"code": "D000068900",
"codingSystem": "MeSH"
}
},
{
"@type": "MedicalCondition",
"name": "Diabetes Mellitus, Type 2",
"code": {
"@type": "MedicalCode",
"code": "C18.452.394.750.149",
"codingSystem": "MeSH"
}
},
{
"@type": "MedicalCondition",
"name": "Cardiovascular Diseases",
"code": {
"@type": "MedicalCode",
"code": "C14",
"codingSystem": "MeSH"
}
}
],
"audience": {
"@type": "MedicalAudience",
"audienceType": "Clinician",
"url": "https://schema.org/Clinician"
},
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "doi",
"value": "10.1016/j.ijcard.2024.11.004"
},
{
"@type": "PropertyValue",
"propertyID": "pmid",
"value": "39874321"
},
{
"@type": "PropertyValue",
"propertyID": "clinical-trial-number",
"value": "NCT04123456"
}
],
"isAccessibleForFree": true,
"license": "https://creativecommons.org/licenses/by/4.0/",
"funder": [
{
"@type": "Organization",
"name": "European Research Council",
"sameAs": "https://ror.org/0472cxd90"
},
{
"@type": "Organization",
"name": "Boehringer Ingelheim",
"url": "https://www.boehringer-ingelheim.com"
}
],
"funding": {
"@type": "Grant",
"name": "ERC Advanced Grant — EMPA-CV Study",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ERC Grant Number",
"value": "ERC-ADG-2022-101054321"
}
},
"keywords": "empagliflozin, SGLT2 inhibitor, type 2 diabetes, cardiovascular outcomes, heart failure, MACE, randomised controlled trial",
"citation": [
{
"@type": "ScholarlyArticle",
"name": "EMPA-REG OUTCOME Investigators. Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes",
"url": "https://doi.org/10.1056/NEJMoa1504720"
},
{
"@type": "ScholarlyArticle",
"name": "Zinman B et al. Empagliflozin and Cardiovascular Outcomes. N Engl J Med. 2015;373:2117-2128",
"sameAs": "https://doi.org/10.1056/NEJMoa1504720"
}
],
"sameAs": [
"https://doi.org/10.1016/j.ijcard.2024.11.004",
"https://pubmed.ncbi.nlm.nih.gov/39874321/"
],
"sdPublisher": {
"@type": "Organization",
"name": "iLoveSchema",
"url": "https://iloveschema.com"
},
"sdDatePublished": "2024-11-20"
}
</script>
This example demonstrates the full depth of a production-quality MedicalScholarlyArticle schema block. The about entities with MeSH codes make the clinical content machine-readable. The ORCID identifiers in author sameAs create verifiable researcher entity links. The isPartOf chain provides the complete bibliographic journal context. The identifier array carries DOI, PMID, and ClinicalTrials.gov registration number. The funding block creates transparent research provenance. And the sdPublisher block — iLoveSchema — identifies the party responsible for generating the structured data.
The sdPublisher Property — Who Is Responsible for the Schema?
The sdPublisher property deserves special attention for medical scholarly content because it addresses a scenario common in academic publishing: a publisher’s web team, a research institution’s digital library team, or a structured data specialist (such as an iLoveSchema client) generates and publishes the schema markup on behalf of the academic authors.
sdPublisher — The organisation or person responsible for generating and publishing the current structured data markup, when this differs from the content’s original authors. Typically used when structured data is derived from published content and published on a different site or by a different party.
For medical publishers using iLoveSchema to generate structured data for their articles:
"sdPublisher": {
"@type": "Organization",
"name": "iLoveSchema",
"url": "https://iloveschema.com"
},
"sdDatePublished": "2026-05-15"
The sdDatePublished companion property declares when the structured data was generated or last updated — distinct from datePublished, which refers to when the article itself was published.
MedicalScholarlyArticle vs Article Schema — When Each Is Appropriate
This distinction matters for any website that publishes both medical scholarly content and general health content.
Use MedicalScholarlyArticle when: the content is a peer-reviewed or formally published scholarly article specifically in the medical domain. Clinical trial reports, systematic reviews, meta-analyses, case reports, clinical practice guidelines, and research letters in medical journals. The key qualifier: it must be scholarly (peer-reviewed, methodologically documented, academically attributed) AND medical (in the domain of healthcare, biomedicine, or clinical science).
Use Article or MedicalWebPage** when: the content is general health information, patient-facing medical explainers, health news articles, or medical blog posts that are not peer-reviewed scholarly publications. A hospital’s patient information leaflet is not a MedicalScholarlyArticle. A news report about clinical trial results is not a MedicalScholarlyArticle. A physician’s editorial opinion piece, unless published in a peer-reviewed journal, is not a MedicalScholarlyArticle.
Use ScholarlyArticle when: the content is peer-reviewed academic scholarship that is NOT specifically in the medical domain — a philosophy paper, a mathematics proof, a social science study, a legal review. The parent type covers scholarly content across all domains.
The MedicalScholarlyArticle type is genuinely narrow: peer-reviewed medical research. It is not a general-purpose type for health content.
Common Medical Scholarly Article Schema Mistakes to Avoid
Mistake 1: Applying MedicalScholarlyArticle to Non-Scholarly Medical Content Health blog posts, patient information, medical news, clinical commentary, and general health guides are not MedicalScholarlyArticle entities. This type is specifically for peer-reviewed scholarly publications in the medical domain. Misapplication violates the semantics of the schema and may produce misleading signals to clinical information systems.
Mistake 2: Omitting publicationType The single most important property unique to this schema type — and the one most commonly omitted. If you only implement one thing from this guide beyond the basics, implement publicationType with the correct MeSH category. It is what distinguishes this from a generic Article implementation.
Mistake 3: Missing MeSH Codes in about Entities Declaring about entities with only name misses the controlled vocabulary benefit. Including MeSH codes transforms a general entity name into a globally referenceable, code-matched medical concept that clinical systems can process with certainty.
Mistake 4: Single Author Without Affiliation Author attribution without institutional affiliation omits one of the most important EEAT signals in medical content. Always include affiliation for each named author. It tells Google and AI systems which institution stands behind the research.
Mistake 5: No DOI or PMID Identifier Medical scholarly articles almost always have a DOI. Publishing schema without declaring the DOI misses the most powerful persistent identifier available. Always include identifier with the DOI as a PropertyValue.
Mistake 6: Applying One Schema Block to Multiple Articles Each article page gets exactly one MedicalScholarlyArticle schema block, populated specifically for that article. Never reuse a template schema block across multiple article pages with placeholder or copied data.
Mistake 7: Marking Retracted Articles as Standard Publications If an article has been retracted, update its schema to include publicationType: "Retracted Publication" and a correction property linking to the retraction notice. Failing to update retracted article schema risks contributing inaccurate medical evidence to AI systems and clinical databases.
Mistake 8: Ignoring Open Access Status isAccessibleForFree and license are frequently omitted but matter greatly for health information systems, library discovery tools, and patients trying to access research. Always declare open access status.
Frequently Asked Questions
What is a Medical Scholarly Article Schema Markup Generator?
A Medical Scholarly Article Schema Markup Generator is a free online tool that converts your medical article’s metadata — title, publication type, abstract, authors, affiliations, MeSH subjects, identifiers, journal information, funding, and access status — into valid MedicalScholarlyArticle JSON-LD structured data code. Instead of writing the complex nested JSON-LD by hand — including the multi-level isPartOf journal hierarchy, MedicalCode sub-objects within about entities, ORCID sameAs links, and Grant funding objects — you fill in a form and the iLoveSchema generator produces clean, validated output. It implements the full Schema.org V30.0 MedicalScholarlyArticle property set, all MeSH publication type values, full MeSH coding for medical subject entities, and ORCID author identity integration.
What content qualifies for MedicalScholarlyArticle schema?
MedicalScholarlyArticle is specifically for peer-reviewed scholarly publications in the medical domain: clinical trials, systematic reviews, meta-analyses, case reports, observational studies, practice guidelines, and research letters published in medical journals or formal academic repositories. General health articles, patient information, health news, and medical blog posts do not qualify. The content must be both scholarly (peer-reviewed, academically attributed, methodologically documented) and medical (in the domain of biomedicine, clinical science, or healthcare).
What is the publicationType property and why is it important?
publicationType is the one property unique to MedicalScholarlyArticle — it is not found in the parent ScholarlyArticle, Article, or CreativeWork types. It accepts values from the US National Library of Medicine’s MeSH publication type catalogue and declares the methodological category of the medical article: Randomized Controlled Trial, Meta-Analysis, Systematic Review, Clinical Trial, Review, Case Reports, Practice Guideline, etc. This classification is critically important for AI health search systems, clinical information databases, and evidence-based medicine platforms that weight evidence differently based on study design.
How do I find MeSH codes for the about entities?
MeSH codes are maintained by the US National Library of Medicine. The searchable MeSH browser is available at meshb.nlm.nih.gov. Search for the drug, condition, procedure, or anatomical structure your article studies. The MeSH tree number (e.g., C18.452.394.750.149 for Type 2 Diabetes Mellitus) is what you include as code.code in the MedicalCode object. For drugs, use the MeSH substance codes from the D (Chemicals and Drugs) tree.
What is ORCID and should I include it?
ORCID (Open Researcher and Contributor ID) is a persistent 16-digit identifier for researchers and academics, maintained by orcid.org. Including an author’s ORCID as a sameAs URL in the Person entity — "sameAs": "https://orcid.org/0000-0001-2345-6789" — creates an unambiguous link between the author named in the schema and their verified global research identity. This resolves the author disambiguation problem (multiple people with the same name) and connects your article schema to the researcher’s complete publication record. Strongly recommended for all named authors who have registered ORCID identifiers.
How should I handle a retracted article?
Update the publicationType to "Retracted Publication" immediately upon retraction. Add a correction property linking to the retraction notice: "correction": "https://doi.org/10.xxxx/retraction-notice". Consider also adding creativeWorkStatus: "Retracted". These updates ensure that clinical information systems and AI platforms recognise the article’s retracted status and do not continue to cite it as valid evidence.
Does Google have specific guidelines for MedicalScholarlyArticle?
Google does not publish a dedicated MedicalScholarlyArticle structured data guide in the same way it publishes guides for Recipe or Product schema. The type falls under Google’s broader medical and health content quality guidelines, which apply heightened E-E-A-T scrutiny to medical content. For medical scholarly publishers, implementing complete MedicalScholarlyArticle schema — particularly author affiliations, publicationType, about entities with MeSH codes, and DOI identifiers — contributes to the E-E-A-T signals Google’s quality systems use to evaluate medical content credibility.
Can I use this schema for preprints?
Yes, with modifications. For preprints (articles posted to servers like medRxiv or bioRxiv before peer review), set creativeWorkStatus: "Draft" or creativeWorkStatus: "Preprint". Set isAccessibleForFree: true and use the appropriate Creative Commons licence. The publicationType should reflect the intended study design (e.g., "Randomized Controlled Trial") but the preprint status should be clearly indicated in description. Note that preprints have not undergone peer review and their evidence weight is categorically different from published RCTs or systematic reviews — structured data should accurately reflect this distinction.
How does this schema help with AI search optimisation for medical content?
AI-powered search and answer platforms actively seek structured, machine-readable medical evidence to ground their health responses. When a clinician asks an AI platform “what is the current evidence for SGLT2 inhibitors in heart failure?” the platform draws on structured entity data — about (the drug and conditions studied), publicationType (the evidence tier), datePublished (recency), author.affiliation (institutional credibility) — to identify and present credible evidence. Medical publishers who implement complete MedicalScholarlyArticle schema are building a structured data foundation that makes their research consistently findable, attributable, and citeable in AI health answer generation — a channel that will only grow in clinical relevance.
