ReviewiScience2026
Intelligent immunotherapy: AI-driven optimization of CAR-T therapy in hematologic malignancies.
Review in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chimeric antigen receptor (CAR)-T cell therapy has transformed outcomes in selected hematologic malignancies, yet its delivery remains constrained by variable efficacy, acute and prolonged toxicities, and complex manufacturing logistics. Artificial intelligence (AI) is being applied across the CAR-T continuum at three distinct levels of evidentiary maturity. Externally validated parsimonious models built on routine clinical and laboratory data including CAR-HEMATOTOX, endothelial activation and stress index (EASIX) and m-EASIX, MyCARe, and gradient-boosting cytokine release syndrome predictors provide deployable risk stratification today. Methodologically sophisticated tools using deep-learning positron emission tomography (PET) segmentation, attention-based digital-pathology analysis, and multimodal fusion of imaging with laboratory and circulating tumor DNA features have demonstrated technical promise but generally lack the multi-center external validation required for clinical-grade deployment. Forward-looking applications, including digital-twin frameworks, large language models, and closed-loop adaptive therapy, remain at the prototype stage and have not yet been validated for CAR-T. This review synthesizes these three tiers, summarizes 18 published AI and statistical-modeling tools in a comparative performance table, and compares data characteristics and AI modeling paradigms across large B cell lymphoma, B cell acute lymphoblastic leukemia, and multiple myeloma. We also identify the implementation realities-absent regulatory clearance, demographic and racial bias in training data, and post-deployment model drift-that condition the field's evolution. The contribution of AI to CAR-T therapy, as the evidence currently stands, is the calibrated quantification of established risk factors rather than the discovery of novel biology, but the methodological and data infrastructure foundations are now in place for the broader transformation that the next decade may enable.
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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.