ReviewNPJ digital medicine2024
Why do probabilistic clinical models fail to transport between sites.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Relapse prediction and individualized treatment-effect modeling in relapsing multiple sclerosis: a systematic review.Frontiers in public health · 2026Pooled it
- Risk Prediction Models for Hospital Readmission After Percutaneous Coronary Intervention: A Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2025Pooled it
- Scoping review of methodology for aiding generalisability and transportability of clinical prediction models.Diagnostic and prognostic research · 2026Review
- A meta-learning method with reduced data requirements for training and updating deep learning models on wearable devices.Communications medicine · 2026Article
- A Simulation-Based Stress-Testing Framework for Evaluating the Transportability of Imaging-Derived Logistic Risk Models Across Cutaneous Lesion Phenotypes.Diagnostics (Basel, Switzerland) · 2026Article
- A real-world feasibility evaluation of LLM-based clinical prediction: emergency department return visit admission across two academic medical centers.Research square · 2026Article
- Deterministic Overlapping Multimorbidity Phenotypes for Leakage-Safe EHR Modeling of Incident Cognitive Impairment in All of Us.Journal of interdisciplinary research applied to medicine · 2026Article
- Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help.Journal of biomedical informatics · 2026Article
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- Comparing methods for handling missing data in electronic health records for dynamic risk prediction of central-line associated bloodstream infection.BMC medical research methodology · 2026Article
- A Holistic Nursing Surveillance Decision Support System for Postoperative Pulmonary Complications After Abdominal Surgery: A Retrospective Cohort Study.Healthcare (Basel, Switzerland) · 2026Article
- Assessing Treatment Effects in Observational Data With Missing Confounders: A Comparative Study of Practical Doubly-Robust and Traditional Missing Data Methods.Statistics in medicine · 2026Article
- Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Gaps in artificial intelligence research for rural health in the United States: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Molecular Diversity, Structure-Function Relationship, Mechanism of Action, and Transformative Potential of Black Soldier Fly Antimicrobial Peptides Against Multidrug-Resistant Pathogens.Current issues in molecular biology · 2026Review
- Task-aligned outcome learning in psychiatry: reducing endpoint dilution.Frontiers in psychiatry · 2026Article
- Attrition and representativeness in development and validation of online symptom checkers-a case study on theFrontiers in artificial intelligence · 2026Article
- When hospitals build their own AI: a comparison of the EU and US regulatory framework.BMJ digital health & AI · 2026Review
- Translational Potential and Explainability of Artificial Intelligence-Based Clinical Decision Support for Adults in Intensive Care: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sites may perform substantially worse at new sites. In this perspective, we argue that we should typically expect this failure to transport, and we present common sources for it, divided into those under the control of the experimenter and those inherent to the clinical data-generating process. Of the inherent sources we look a little deeper into site-specific clinical practices that can affect the data distribution, and propose a potential solution intended to isolate the imprint of those practices on the data from the patterns of disease cause and effect that are the usual target of probabilistic clinical models.
Identifiers
What Socratic 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 Socratic graph.