ArticleBlood neoplasia2026
Artificial intelligence-based prognostic models in acute myeloid leukemia: systematic review and meta-analysis.
Article in Blood neoplasia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Artificial Intelligence for Personalized Management of Acute Myeloid Leukemia.Journal of personalized medicine · 2026Review
- Evolving Management Approaches Toward Personalized Therapy in Acute Myeloid Leukemia: A Narrative Review.Journal of personalized medicine · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning and deep learning tools have been proposed to improve survival prediction in acute myeloid leukemia (AML), but comparative benchmarks remain unclear. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 searches of PubMed, Scopus, and Web of Science (January 2018 to March 2025) identified studies developing or externally validating artificial intelligence (AI)-based models for overall or relapse-free survival reporting area under the receiver operating characteristic (ROC) area under the curve (AUC). Two reviewers extracted design, population, features, algorithms, and training/validation AUCs and assessed risk of bias using Prediction model Risk of Bias Assessment Tool (PROBAST). Random-effects meta-analysis (DerSimonian-Laird) pooled validation AUCs overall and by horizon (1/2/3/5 years) and feature category (gene-centric vs nongenetic). Optimism bias was the training-validation AUC difference. We included 24 predominantly retrospective studies (137 model cohorts; ∼51 055 patients). Of 120 PROBAST domain ratings, 74% were low risk, 25% unclear, and <1% high; statistical analysis was the weakest domain. Across 73 independent validation cohorts, the pooled AUC was 0.769 (95% confidence interval [CI], 0.742-0.795) with substantial between-study variability (
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.