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. 2022 Oct 18;26(1):315.
doi: 10.1186/s13054-022-04197-5.

Algorithmic fairness audits in intensive care medicine: artificial intelligence for all?

Davy van de Sande  1 , Jasper van Bommel  2 , Eline Fung Fen Chung  2 , Diederik Gommers  2 , Michel E van Genderen  2
Affiliations

Algorithmic fairness audits in intensive care medicine: artificial intelligence for all?

Davy van de Sande et al. Crit Care. .
No abstract available

Keywords: Artificial intelligence; Bias; Equity; Intensive care.

PubMed Disclaimer

Conflict of interest statement

DGo has received speakers fees and travel expenses from Dräger, GE Healthcare (medical advisory board 2009–12), Maquet, and Novalung (medical advisory board 2015–18). All other authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Schematic overview of the intensive care medicine artificial intelligence fairness audit. Conventional clinical patient data (e.g., vital signs, laboratory values, and demographics) are typically used to train an AI algorithm and its performance is then evaluated on an internal or external test dataset to see whether it works in the first place. Next, the fairness audit should take place: evaluate model performance across multiple subpopulations (for example, based on ethnicity, age, gender, or other characteristics). If concerns regarding algorithmic fairness arise, re-training and/or re-calibration should be considered (go/no-go). *Protected personal characteristics such as ethnicity, socioeconomic information, and others need to be collected in patient health records. AI = artificial intelligence
See this image and copyright information in PMC

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