ChatGPT for Automated Cross‐Checking of Authors' Conflicts of Interest Against Industry Payments

Abstract

Abstract Objective The Centers for Medicare & Medicaid Services “OpenPayments” database tracks industry payments to US physicians to improve research conflicts of interest (COIs) transparency, but manual cross‐checking of articles' authors against this database is labor‐intensive. This study aims to assess the potential of large language models (LLMs) like ChatGPT to automate COI data analysis in medical publications. Study Design An observational study analyzing the accuracy of ChatGPT in automating the cross‐checking of COI disclosures in medical research articles against the OpenPayments database. Setting Publications regarding Food and Drug Administration‐approved biologics for chronic rhinosinusitis with nasal polyposis: omalizumab, mepolizumab, and dupilumab. Methods First, ChatGPT evaluated author affiliations from PubMed to identify those based in the United States. Second, for author names matching 1 or multiple payment recipients in OpenPayments, ChatGPT undertook a comparative analysis between author affiliation and OpenPayments recipient metadata. Third, ChatGPT scrutinized full article COI statements, producing an intricate matrix of disclosures for each author against each relevant company (Sanofi, Regeneron, Genentech, Novartis, and GlaxoSmithKline). A random subset of responses was manually checked for accuracy. Results In total, 78 relevant articles and 294 unique US authors were included, leading to 980 LLM queries. Manual verification showed accuracies of 100% (200/200; 95% confidence interval [CI]: 98.1%‐100%) for country analysis, 97.4% (113/116; 95% CI: 92.7%‐99.1%) for matching author affiliations with OpenPayments metadata, and 99.2% (1091/1100; 95% CI: 98.5%‐99.6%) for COI statement data extraction. Conclusion LLMs have robust potential to automate author‐company‐specific COI cross‐checking against the OpenPayments database. Our findings pave the way for streamlined, efficient, and accurate COI assessment that could be widely employed across medical research.

Ähnliche Arbeiten