AI-enabled GRADE: How the GRADE Working Group will use automation to rate the certainty of evidence of intervention effects

Journal of Clinical Epidemiology · Published 2026-07-01 · DOI 10.1016/j.jclinepi.2026.112411

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Authors (35)

Bernardo Sousa-Pinto, Manuel Marques-Cruz, Rafael José Vieira, Vítor Henrique Duarte, Camila Ávila-Oliver, Andreea Dobrescu, Paweł Jemioło, Arnav Agarwal, Malgorzata M. Bala, Jessica Beltran, Antonio Bognanni, Fabio Cruciani, Omar Dewidar, Sara Gil-Mata, Changhao Liang, Daniel Martinho-Dias, Claus Nowak, Honoria Ocagli, Paula Perestrelo, Ana Carolina Pereira Nunes Pinto, Joana Reis-Pardal, Pau Riera-Serra, Aline Rocha, Karina Fernández-Saénz, Itziar Etxeandia-Ikobaltzeta, Curtis S. Harrod, Miranda W. Langendam, Joerg J. Meerpohl, Francesco Nonino, Wojtek Wiercioch, Ignacio Neumann, Ariel Izcovich, Silvia Minozzi, Gerald Gartlehner, Holger J. Schünemann

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Publication details

Year
2026

Citation

Sousa-Pinto, B., Marques-Cruz, M., Vieira, R., et al. (2026). AI-enabled GRADE: How the GRADE Working Group will use automation to rate the certainty of evidence of intervention effects. Journal of Clinical Epidemiology. https://doi.org/10.1016/j.jclinepi.2026.112411

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