PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
Abstract
The Prediction model Risk Of Bias ASsessment Tool (PROBAST) is used to assess the quality, risk of bias, and applicability of prediction models or algorithms and of prediction model/algorithm studies. Since PROBAST’s introduction in 2019, much progress has been made in the methodology for prediction modelling and in the use of artificial intelligence, including machine learning, techniques. An update to PROBAST-2019 is thus needed. This article describes the development of PROBAST+AI. PROBAST+AI consists of two distinctive parts: model development and model evaluation. For model development, PROBAST+AI users assess quality and applicability using 16 targeted signalling questions. For model evaluation, PROBAST+AI users assess the risk of bias and applicability using 18 targeted signalling questions. Both parts contain four domains: participants and data sources, predictors, outcome, and analysis. Applicability of the prediction model is rated for the participants and data sources, predictors, and outcome domains. PROBAST+AI may replace the original PROBAST tool and allows all key stakeholders (eg, model developers, AI companies, researchers, editors, reviewers, healthcare professionals, guideline developers, and policy organisations) to examine the quality, risk of bias, and applicability of any type of prediction model in the healthcare sector, irrespective of whether regression modelling or AI techniques are used.
Author
Moons, Karel G M
Damen, Johanna A A
Kaul, Tabea
Hooft, Lotty
Andaur Navarro, Constanza
Dhiman, Paula
Beam, Andrew L
Van Calster, Ben
Celi, Leo Anthony
Denaxas, Spiros
Denniston, Alastair K
Ghassemi, Marzyeh
Heinze, Georg
Kengne, André Pascal
Maier-Hein, Lena
Liu, Xiaoxuan
Logullo, Patricia
McCradden, Melissa D
Liu, Nan
Oakden-Rayner, Lauren
Singh, Karandeep
Ting, Daniel S
Wynants, Laure
Yang, Bada
Reitsma, Johannes B
Riley, Richard D
Collins, Gary S
van Smeden, Maarten
Damen, Johanna A A
Kaul, Tabea
Hooft, Lotty
Andaur Navarro, Constanza
Dhiman, Paula
Beam, Andrew L
Van Calster, Ben
Celi, Leo Anthony
Denaxas, Spiros
Denniston, Alastair K
Ghassemi, Marzyeh
Heinze, Georg
Kengne, André Pascal
Maier-Hein, Lena
Liu, Xiaoxuan
Logullo, Patricia
McCradden, Melissa D
Liu, Nan
Oakden-Rayner, Lauren
Singh, Karandeep
Ting, Daniel S
Wynants, Laure
Yang, Bada
Reitsma, Johannes B
Riley, Richard D
Collins, Gary S
van Smeden, Maarten
Date
2025-03-24
Type
Article
Subject
Algorithms, Artificial intelligence, Machine learning
Collections
Citation
Moons KGM, Damen JAA, Kaul T, Hooft L, Andaur Navarro C, Dhiman P, Beam AL, Van Calster B, Celi LA, Denaxas S, Denniston AK, Ghassemi M, Heinze G, Kengne AP, Maier-Hein L, Liu X, Logullo P, McCradden MD, Liu N, Oakden-Rayner L, Singh K, Ting DS, Wynants L, Yang B, Reitsma JB, Riley RD, Collins GS, van Smeden M. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ. 2025 Mar 24;388:e082505. doi: 10.1136/bmj-2024-082505.
Journal / Source Title
the bmj
DOI
10.1136/bmj-2024-082505
PMID
40127903
Publisher
British Medical Association
Publisher’s URL
https://www.bmj.com/