ArticleJournal of the American Medical Informatics Association : JAMIA2025
AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Health Education and Practice: A Systematic Review of Health Students' and Academics' Knowledge, Perceptions and Experiences.International nursing review · 2025Pooled it
- Fuzzy min-max dual motif-guided heterogeneous Dandelion graph multi-scale residual attention network for healthcare AI misclassification reduction.Scientific reports · 2026Article
- Clinical trials for continuously monitored and updated AI systems.Nature medicine · 2026Article
- Is AI actually improving healthcare?Nature medicine · 2026Article
- Clinical Artificial Intelligence Agents in Nephrology: From Prediction to Action Through Workflow-Native Intelligence-A Roadmap for Workflow-Integrated Care.Journal of clinical medicine · 2026Review
- Fairness-aware K-means clustering in digital mental health for higher education students: a generalizable framework for equitable clustering.JAMIA open · 2026Article
- The spatial revolution in immuno-oncology: artificial intelligence decoding NK cell niches to predict therapeutic response.Frontiers in immunology · 2026Review
- Knowledge, Attitude, and Perception of Artificial Intelligence in Healthcare Among Postgraduate Residents: A Cross-sectional Survey.Oman medical journal · 2026Article
- The evaluation illusion of large language models in medicine.NPJ digital medicine · 2025Article
- Feedback loops in intensive care unit prognostic models: an under-recognised threat to clinical validity.The Lancet. Digital health · 2025Review
- Deep learning model to identify and validate hypotension endotypes in surgical and critically ill patients. Comment on Br J Anaesth 2025; 134: 308-16.British journal of anaesthesia · 2025Article
- NeoPred: dual-phase CT AI forecasts pathologic response to neoadjuvant chemo-immunotherapy in NSCLC.Journal for immunotherapy of cancer · 2025Article
- Research Advance of Causal Inference in Clinical Medicine: A Bibliometrics Analysis via Citespace.Journal of multidisciplinary healthcare · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
Abstract
The primary practice of healthcare artificial intelligence (AI) starts with model development, often using state-of-the-art AI, retrospectively evaluated using metrics lifted from the AI literature like AUROC and DICE score. However, good performance on these metrics may not translate to improved clinical outcomes. Instead, we argue for a better development pipeline constructed by working backward from the end goal of positively impacting clinically relevant outcomes using AI, leading to considerations of causality in model development and validation, and subsequently a better development pipeline. Healthcare AI should be "actionable," and the change in actions induced by AI should improve outcomes. Quantifying the effect of changes in actions on outcomes is causal inference. The development, evaluation, and validation of healthcare AI should therefore account for the causal effect of intervening with the AI on clinically relevant outcomes. Using a causal lens, we make recommendations for key stakeholders at various stages of the healthcare AI pipeline. Our recommendations aim to increase the positive impact of AI on clinical outcomes.
Indexed as
Identifiers
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.