Year: 2025
Type: article
Abstract: Background The use of artificial intelligence or machine learning in the development of prediction models is increasing exponentially but the present model is associated with a high degree of heterogeneity and associated bias. The present model is associated with a difficult learning curve and we aimed to develop a tool evaluating risk of bias in cardiology research which was succinct and effective. Methods Our tool (AIr) consists of 10 questions and can be utilised to assess the risk of bias in model development, external validation, and the combination of the two in machine-learned or artificial intelligence models. Results AIr was as effective as the current risk of bias tool, PROBAST, however, was significantly more succinct and had a greater inter-rater reliability than PROBAST. Conclusion We propose that our tool maintains validity regarding the assessment of the risk of bias in cardiology publications whilst increasing reliability when compared with PROBAST.
Source: F1000Research
Authors: Hannah Glatzel, Solange Bramer, Katie Malone, Ranu Baral, Muhammad Hamza Shah, Sakshi Roy, Arjun Ahluwalia, Kirshun Suvendiran, Joanne Lac, Girishkumar Sivakumar, YuZhi Phuah, Lily Snell, Karish Thavabalan, Carmen Lucia Garcia Pérez, Reubeen Ahmad, Niraj S Kumar, Mahmood Ahmad
Institutions: Great Western Hospital, Great Western Hospitals NHS Foundation Trust, Barnet Hospital, Whipps Cross University Hospital, King's College Hospital NHS Foundation Trust, King's College Hospital, Queen's University Belfast, University College London, University College London Hospitals NHS Foundation Trust, Brighton and Sussex Medical School, University of Leicester, Royal Free London NHS Foundation Trust
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,350
OpenAlex ID: W4415818260