ReviewMedComm2026
CAR-T Cells: Current Status, Challenges, and Future Prospects.
Review in MedComm, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As chimeric antigen receptor (CAR)-T cell therapy has expanded rapidly to meet the growing global cancer burden; many challenges have emerged as a critical factor influencing its efficacy. However, due to the complicated mechanisms of CAR-T cells, human interference alone was insufficient to optimize the outcomes. In parallel, artificial intelligence (AI) has begun to intersect with CAR-T cells, offering novel computational interferences that can refine therapeutic mechanisms. The literature is still lacking a comprehensive investigation that merges CAR-T cell mechanistic biology and limitations with the advancing abilities of AI to meet these barriers. This review provides an overview of the mechanistic foundations of CAR-T cell. It also investigates the various challenges facing the current CAR-T therapies including toxicity, resistance, and accessibility issues. On this basis, we examined the way AI-based innovations are being utilized to optimize the CAR-T engineering and clinical management. Finally, we examined clinical studies and case studies incorporating AI elements, emphasizing both therapeutic mechanisms and outcomes of the study. By integrating mechanistic biology with computational innovation, this review provides a unified unique perspective that can guide the development of safer and more effective CAR-T therapies.
Indexed as
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.