ArticleJournal of evaluation in clinical practice2026
How Can Clinicians Decide if AI-Enabled Risk Prediction Models Are Fit for Purpose for Real World Use?
Article in Journal of evaluation in clinical practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
Artificial intelligence (AI)-enabled risk prediction models are being increasingly used in hospital practice to predict patient risk of adverse events, aimed at giving early warning of impending events and facilitating preventive intervention. However, evidence of beneficial impact varies which may be due to clinicians not finding them useful because of suboptimal predictive accuracy (effectiveness), burden of false alerts (efficiency) or narrow prediction windows (utility). Current model performance measures do not capture the interdependency of these three dimensions. In this commentary, using sepsis risk prediction as an example, we present methods that assist clinicians to: assess the suitability of particular models and decide ideal decision thresholds; optimise model performance; and configure and deliver alerts to frontline clinicians.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.