Post-market surveillance for literature on QuantiFERON TB Gold Plus: what improvements can artificial intelligence bring?

Abstract

BACKGROUND: Post-market surveillance (PMS) under the European Union In Vitro Diagnostic Regulation (IVDR) demands proactive, literature-based evidence, but mature assays like QuantiFERON TB Gold Plus (QFT-Plus) generate volumes of peer-reviewed and other literature that can strain manual workflows. METHODS: We ran a comparative study of an AI-enabled literature-surveillance platform (jointly developed with Huma.ai called the Huma.ai Platform) versus manual search for QFT-Plus PMS. PubMed and PubMed Central were queried for publications in 2024; human studies published in English underwent duplicate screening and full-text appraisal. Outcomes were yield, precision, overlap/unique entries, and reviewer time. RESULTS: The Huma.ai Platform retrieved 673 records, with 661 relevant to screening (98.21% precision). Manual searching retrieved 111, with 106 relevant to screening (95.50% precision): there were 103 shared and three manual-only items (metadata gaps). The Huma.ai Platform contributed 561 unique papers, 5 of which were excluded after full-text appraisal. In total, 664 articles were evaluated; no new safety signals were identified. Screening time averaged ∼16 s per article with Huma.ai Platform versus ∼60 s manually; full-text time (∼15 min per article) was similar. CONCLUSION: AI-assisted surveillance substantially increases coverage and reduces screening effort while maintaining high precision. Thus it supports efficient, reproducible PMS for QFT-Plus.

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