Citation and selected extracts below.
CITATION: AI for detecting paper mill papers
BMJ 2026; 392 doi: https://doi.org/10.1136/bmj.s564 (Published 26 March 2026)
Cite this as: BMJ 2026;392:s564
Cristina Candal-Pedreira, Alberto Ruano-Ravina
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An important step towards improving research integrity
Paper mills are a systemic threat to research integrity, contaminating the evidence, influencing citations, and potentially affecting clinical decision making. Paper mills are companies that generate manuscripts engineered to resemble legitimate scientific articles, frequently relying on fabricated, manipulated, or duplicated data and images. Authorship of those manuscripts is sold to authors and submitted to scientific journals for publication as if they were genuine scholarly work...
In this context, a linked study by Scancar and colleagues (doi:10.1136/bmj-2025-087581) is a timely and necessary contribution. The authors show that large scale detection might be feasible using machine learning approaches trained to identify recurrent linguistic and structural patterns...
Transparency measures could help realign incentives; for example, displaying journal level retraction counts and rates alongside impact indicators, or clearly flagging retracted publications and expressions of concern within researcher profiles (eg, ORCID and Web of Science ResearcherID records)...
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HIFA profile: Neil Pakenham-Walsh is coordinator of HIFA (Healthcare Information For All), a global health community that brings all stakeholders together around the shared goal of universal access to reliable healthcare information. HIFA has 20,000 members in 180 countries, interacting in four languages and representing all parts of the global evidence ecosystem. HIFA is administered by Global Healthcare Information Network, a UK-based nonprofit in official relations with the World Health Organization. Email: neil@hifa.org