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Review
. 2025 Aug 5:7:1633458.
doi: 10.3389/fdgth.2025.1633458. eCollection 2025.

Applications of generative artificial intelligence in outcome prediction in intensive care medicine-a scoping review

Tanja Stamm  1   2 , Mohamed Bader-El-Den  2 , James McNicholas  3 , Jim Briggs  2 , Peng Zhao  2
Affiliations
Review

Applications of generative artificial intelligence in outcome prediction in intensive care medicine-a scoping review

Tanja Stamm et al. Front Digit Health. .

Abstract

When a patient survives the first 24 h in intensive care, outcome prediction is crucial for further treatment decisions. As recent advances have shown that Artificial Intelligence (AI) outperforms clinicians in prognostication, and especially generative AI has developed rapidly in the past ten years, this scoping review aimed to explore the use of generative AI models for outcome prediction in intensive care medicine. Of the 481 records found in the search, 119 studies were subjected to abstract screening and, when necessary, full-text review for eligibility assessment. Twenty-two studies and two review articles were finally included. The studies were categorized into three prototypical use cases for generative AI in outcome prediction in intensive care: (i) data augmentation, (ii) feature generation from unstructured data, and (iii) prediction by the generative model. In the first two use cases, the generative models worked together with downstream predictive models. In the third use case, the generative models made the predictions themselves. The studies within data augmentation either fell into the area of compensation for class imbalances by producing additional synthetic cases or imputation of missing values. Overall, Generative Adversarial Network (GAN) was the most frequently used technology (8/22 studies; 36%), followed by Generative Pretrained Transformer (GPT) (7/22 studies; 32%). All publications except one were from the last four years. This review shows that generative AI has immense potential in the future, and continuous monitoring of new technologies is necessary to ensure that patients receive the best possible care.

Keywords: comorbidity; critical care; generative adversarial network; large language model; mortality; survival.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Two-level keyword tree and research field classification diagram.
Figure 2
Figure 2
PRISMA Flow Chart for study selection and assessment.
Figure 3
Figure 3
Prototypical use cases for the use of generative AI in outcome prediction in intensive care medicine.
See this image and copyright information in PMC

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