ReviewResearch and practice in thrombosis and haemostasis2025
Artificial intelligence in clinical thrombosis and hemostasis: A review.
Review in Research and practice in thrombosis and haemostasis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- A machine learning-based risk prediction model for Hospitalized patients with deep vein thrombosis.PeerJ · 2026Article
- Venous Thromboembolism Management and Anticoagulation Opportunities in Cancer Patients.European cardiology · 2026Review
- Managing patients with a history of arterial disease and new venous thromboembolism.Hematology. American Society of Hematology. Education Program · 2025Article
- Artificial intelligence in hematology: current trends and application areas.Annals of hematology · 2025Review
- Artificial intelligence in antiphospholipid syndrome: toward individualized risk prediction.Research and practice in thrombosis and haemostasis · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial Intelligence (AI) and machine learning (ML) are transforming hemostasis and thrombosis care, with applications spanning disease detection, risk assessment, laboratory testing, patient education, personalized medicine, and drug development. This narrative review explores AI's clinical utility and limitations across these 6 domains. Methods: A comprehensive search of PubMed, Embase, and Scopus (up to February 2025) was conducted using terms related to AI, thrombosis, and hemostasis. Peer-reviewed, English-language studies were included, supplemented by manual and reference screening. Of 84 studies included, 38 focused on risk assessment, 16 on diagnostics, and others on personalized medicine, drug development, and patient engagement. Results: AI demonstrated high accuracy in diagnosing thrombotic events via imaging and electronic health record analysis, although sensitivity gaps persisted for complex cases. In laboratory settings, AI outperformed manual review in detecting errors (eg, sample mislabeling and clotted specimens). Risk stratification models surpassed traditional scores (eg, CHA Conclusion: AI offers significant promise for improving diagnostics, risk prediction, and individualized therapy in thrombosis and haemostasis. Future integration depends on transparent, validated, and equitable AI systems embedded within clinical workflows.
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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.