The fundamental problem of risk prediction for individuals: health AI, uncertainty, and personalized medicine
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arXiv
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| Format: | Preprint |
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2025
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| author | Barreñada, Lasai Steyerberg, Ewout W Timmerman, Dirk Thomassen, Doranne Wynants, Laure Van Calster, Ben |
| author_facet | Barreñada, Lasai Steyerberg, Ewout W Timmerman, Dirk Thomassen, Doranne Wynants, Laure Van Calster, Ben |
| contents | Background and Objective: Clinical prediction models are commonly evaluated regarding performance for a population, although decisions are made for individuals. The classic view relates uncertainty in risk estimates for individuals to sample size (estimation uncertainty) while other sources are model uncertainty (variability in modeling choices) and applicability uncertainty (variability in measurement procedures and between populations). We aim to illustrate the uncertainty of prediction models in estimating individual risks with an ovarian cancer example. Methods: We used real and synthetic data for ovarian cancer diagnosis to train 59400 models with variations in estimation, model, and applicability uncertainty. We then used these models to estimate the probability of ovarian cancer in a fixed test set of 100 patients and evaluate the variability in individual estimates. Results: We show empirically that estimation uncertainty can be strongly dominated by model uncertainty and applicability uncertainty, even for models that perform well at the population level. Estimation uncertainty decreased considerably with increasing training sample size, whereas model and applicability uncertainty remained large. Conclusion: Individual risk estimates are far more uncertain than often assumed. Model uncertainty and applicability uncertainty usually remain invisible when prediction models or algorithms are based on a single study. Predictive algorithms should inform, not dictate, care and support personalization through clinician-patient interaction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The fundamental problem of risk prediction for individuals: health AI, uncertainty, and personalized medicine Barreñada, Lasai Steyerberg, Ewout W Timmerman, Dirk Thomassen, Doranne Wynants, Laure Van Calster, Ben Methodology Background and Objective: Clinical prediction models are commonly evaluated regarding performance for a population, although decisions are made for individuals. The classic view relates uncertainty in risk estimates for individuals to sample size (estimation uncertainty) while other sources are model uncertainty (variability in modeling choices) and applicability uncertainty (variability in measurement procedures and between populations). We aim to illustrate the uncertainty of prediction models in estimating individual risks with an ovarian cancer example. Methods: We used real and synthetic data for ovarian cancer diagnosis to train 59400 models with variations in estimation, model, and applicability uncertainty. We then used these models to estimate the probability of ovarian cancer in a fixed test set of 100 patients and evaluate the variability in individual estimates. Results: We show empirically that estimation uncertainty can be strongly dominated by model uncertainty and applicability uncertainty, even for models that perform well at the population level. Estimation uncertainty decreased considerably with increasing training sample size, whereas model and applicability uncertainty remained large. Conclusion: Individual risk estimates are far more uncertain than often assumed. Model uncertainty and applicability uncertainty usually remain invisible when prediction models or algorithms are based on a single study. Predictive algorithms should inform, not dictate, care and support personalization through clinician-patient interaction. |
| title | The fundamental problem of risk prediction for individuals: health AI, uncertainty, and personalized medicine |
| topic | Methodology |
| url | https://arxiv.org/abs/2506.17141 |