ArticleNature communications2020
Improving the accuracy of medical diagnosis with causal machine learning.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 146 papers, 3 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
146 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Assessing the precision of machine learning for diagnosing pulmonary arterial hypertension: a systematic review and meta-analysis of diagnostic accuracy studies.Frontiers in cardiovascular medicine · 2024Pooled it
- Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease.Alzheimer's research & therapy · 2023Pooled it
- Does deidentification of data from wearable devices give us a false sense of security? A systematic review.The Lancet. Digital health · 2023Pooled it
- Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi-center study.Headache · 2022Trial
- Symptom-Driven Prediction of Chronic Multimorbidity with a Macro-Micro Heterogeneous Graph Neural Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Conditional Perplexity Scoring for Large Language Model-Generated Differential Diagnoses in Case Reports: Preliminary Computational Evaluation.JMIR formative research · 2026Article
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- Developing a prediction model to predict the incidence of metastasis in lung cancer patients using artificial intelligence techniques.BMC pulmonary medicine · 2026Article
- Diagnostic accuracy and citation integrity of four large language models on otolaryngology vignettes.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Oral squamous cell carcinoma diagnosis: an interpretable deep learning approach using multi-scale pathological images.BMC oral health · 2026Article
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Using causal machine learning and real world data to improve dose response decision making for secukinumab in psoriatic arthritis.Scientific reports · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Early detection of chronic kidney disease based on a SURD-enhanced machine learning model.Scientific reports · 2026Article
- Explainable artificial intelligence for molecular design in pharmaceutical research.Chemical science · 2026Review
- Perception of artificial intelligence and machine learning applications in the Nigerian healthcare sector: A cross-sectional study.PLOS global public health · 2026Article
- Early metoprolol use in ICU patients with congestive heart failure is associated with increased 30-day mortality: a causal machine learning study.Frontiers in pharmacology · 2026Article
- AI-driven quality enhancement of medical datasets: a closed-loop framework of synthesis, annotation, quality control, and integration.Frontiers in medicine · 2026Review
- Toward precision stroke rehabilitation: an integrated causal machine learning and clinician feedback approach.JAMIA open · 2025Article
- LSTM-assisted optical fiber interferometric sensing: breaking the limitation of free spectral range.Light, science & applications · 2025Article
86 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patients symptoms. We show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algorithms. We compare our counterfactual algorithms to the standard associative algorithm and 44 doctors using a test set of clinical vignettes. While the associative algorithm achieves an accuracy placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. Our results show that causal reasoning is a vital missing ingredient for applying machine learning to medical diagnosis.
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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.