ArticlePatterns (New York, N.Y.)2025
When accurate prediction models yield harmful self-fulfilling prophecies.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Structural requirements for intelligent clinical digital twins in feedback-driven care.npj health systems · 2026Review
- Article
- Multicenter validation and updating of the ELDER-ICU model for severity assessment in elderly critical illness.NPJ digital medicine · 2026Article
- The Ethics of Leveraging Routinely Collected Patient Data for AI Development: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Feedback loops in intensive care unit prognostic models: an under-recognised threat to clinical validity.The Lancet. Digital health · 2025Review
- Can an accurate model be bad?Patterns (New York, N.Y.) · 2025Article
- When accurate prediction models yield harmful self-fulfilling prophecies.Patterns (New York, N.Y.) · 2025Article
- AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- From algorithms to action: improving patient care requires causality.BMC medical informatics and decision making · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction models are popular in medical research and practice. Many expect that by predicting patient-specific outcomes, these models have the potential to inform treatment decisions, and they are frequently lauded as instruments for personalized, data-driven healthcare. We show, however, that using prediction models for decision-making can lead to harm, even when the predictions exhibit good discrimination after deployment. These models are harmful self-fulfilling prophecies: their deployment harms a group of patients, but the worse outcome of these patients does not diminish the discrimination of the model. Our main result is a formal characterization of a set of such prediction models. Next, we show that models that are well calibrated before and after deployment are useless for decision-making, as they make no change in the data distribution. These results call for a reconsideration of standard practices for validation and deployment of prediction models that are used in medical decisions.
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What OpenQuestion holds
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.