AI in myeloproliferative neoplasms can grade marrow fibrosis and predict survival, but authors say prospective, outcome-focused trials are needed first.
Artificial narrative review published in .1 Of the applications the authors examined, only tools that estimate marrow fibrosis from a standard blood count had been tested prospectively.
Investigators from Italy, the United Kingdom, and Spain searched PubMed/MEDLINE through August 2026 and also screened reference lists plus conference abstracts from the platform produced an AUC of 0.94 (macro-averaged) for subtyping in an external cohort of 96 patients, and a foundation model trained on 200 biopsies from Florence, Italy, distinguished pre-PMF from ET with an AUC of 0.89 but a sensitivity of 66.6%.
Blood Count Models Reach Prospective Testing
FIBOM-AI, which uses 27 complete blood count variables plus age to flag grade 2 to 3 reticulin fibrosis, was built in 6 French centers and externally validated in 7 others, posting an AUC of 0.92 in the external cohort and an accuracy of 85.2% when applied prospectively in routine practice.1,2 Its developers framed it as a triage tool rather than a substitute for biopsy, because it neither assigns an MPN subtype nor detects pre-PMF.
For prognosis, the AI-based prognostic score AIPSS-MF, a random survival forest model using 8 clinical variables, achieved a concordance index of 0.814 among 511 patients, compared with 0.724 for the International Prognostic Scoring System and 0.654 for MIPSS70.1,3 However, the authors pointed out that the legacy scores were not recalibrated and that systematic reviews of tabular prognostic data have not found that machine learning consistently outperforms carefully specified regression models.1 Therapy response remains the least developed area; no machine learning model has been validated to predict JAK inhibitor response in MF, and a phase 4 study designed to test hydroxyurea-resistance predictors in polycythemia vera was terminated without published results.
“High discrimination is necessary but not sufficient: with few exceptions, the studies reviewed here establish the former while leaving the latter untested,” the authors wrote.
Review Limitations and Research Priorities
Because this was a narrative, nonsystematic review, the authors noted the possibility of bias in which studies were chosen and how they were interpreted. They did not perform a meta-analysis and reported performance metrics as published rather than recalculating them. They also noted that the underlying studies rarely reported calibration, decision-curve analysis, or performance by age, sex, ethnicity, and care setting, leaving the risk of unequal performance across groups unquantified.
The authors called for prospective multicenter validation with head-to-head comparisons against standard scores, clinical-utility trials, federated learning, and shared benchmark datasets to offset small sample sizes. They added that AI software informing diagnosis or treatment is typically regulated as a medical device and should be monitored for performance drift after approval.
“Bridging the gap between technical progress and demonstrated clinical utility will require coordinated effort from hematologists, pathologists, data scientists, and regulators, together with prospective, outcome-oriented validation studies,” the authors concluded.
References
1. Duminuco A, Harrington P, Mosquera Orgueira A, et al. <a Wolverine Peptide' Use Rose 33-Fold Despite Scant Evidence
