Journal article
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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 261.5KB, Terms of use)
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- Publisher copy:
- 10.1136/bmj-2024-082505
Authors
+ Cancer Research UK
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- Funder identifier:
- https://ror.org/054225q67
- Grant:
- 27294
+ Medical Research Council
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- Funder identifier:
- https://ror.org/03x94j517
- Grant:
- MR/V038168/1
+ Engineering and Physical Sciences Research Council
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- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/Y018516/1
- Publisher:
- BMJ Publishing Group
- Journal:
- British Medical Journal More from this journal
- Volume:
- 388
- Article number:
- e082505
- Place of publication:
- England
- Publication date:
- 2025-03-24
- Acceptance date:
- 2025-01-16
- DOI:
- EISSN:
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0959-8138
- ISSN:
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1759-2151
- Pmid:
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40127903
- Language:
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English
- Pubs id:
-
2100914
- Local pid:
-
pubs:2100914
- Deposit date:
-
2025-05-02
- ARK identifier:
Terms of use
- Copyright holder:
- Moons et al
- Copyright date:
- 2025
- Rights statement:
- ©2025 The Authors. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.
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